{"hits":{"hits":[{"created":"2019-12-11T00:00:00+00:00","updated":"2023-03-07T02:57:44.077596+00:00","id":"1769970","links":{"bibtex":"https://inspirehep.net/api/literature/1769970?format=bibtex","latex-eu":"https://inspirehep.net/api/literature/1769970?format=latex-eu","latex-us":"https://inspirehep.net/api/literature/1769970?format=latex-us","json":"https://inspirehep.net/api/literature/1769970?format=json","json-expanded":"https://inspirehep.net/api/literature/1769970?format=json-expanded","cv":"https://inspirehep.net/api/literature/1769970?format=cv","citations":"https://inspirehep.net/api/literature/?q=refersto%3Arecid%3A1769970"},"metadata":{"documents":[{"filename":"PoS(LHCP2019)260.pdf","attachment":{"content":"P\no\nS\n(\nL\nH\nC\nP\n2\n0\n1\n9\n)\n2\n6\n0\n\nOpen Data at CMS: Status and Plans\n\nThomas McCauley∗ on behalf of the CMS Collaboration\nUniversity of Notre Dame (US)\nE-mail: thomas.mccauley@cern.ch\n\nThe CMS Experiment at the LHC has released many large datasets of proton-proton collision data\nas well as simulation to the public as part of its commitment to data preservation and open access.\nThe collision data released totals around 2 fb−1 at 7 TeV and nearly 12 fb−1 at 8 TeV.\nData preservation describes the efforts to not only preserve the data itself but the conditions in\nwhich it can be analyzed. This requires archiving and documenting information such as run\nenvironment and conditions and tools such as analysis software and workflows. Open access\ndescribes the efforts to share these tools and information with the public, including educators and\nresearchers.\nThese efforts present many challenges, partly due to the complexity and size of the CMS datasets\nand also to the knowledge required to make meaningful use of them. We describe here how we\ndealt with these challenges and the usage of CMS open data not only in public education and\nengagement but also in fundamental research. Furthermore we describe plans for future releases\nof data.\n\n7th Annual Conference on Large Hadron Collider Physics - LHCP2019\n20-25 May, 2019\nPuebla, Mexico\n\n∗Speaker.\n\nc© Copyright owned by the author(s) under the terms of the Creative Commons\nAttribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). https://pos.sissa.it/\n\nmailto:thomas.mccauley@cern.ch\n\n\nP\no\nS\n(\nL\nH\nC\nP\n2\n0\n1\n9\n)\n2\n6\n0\n\nCMS Open Data Thomas McCauley\n\n1. Introduction\n\nDuring Runs 1 and 2 of the Large Hadron Collider (LHC) over the years 2010 to 2018 the\nCMS Experiment [1] has collected over 6 fb−1 of proton-proton collision data at\n\n√\ns = 7 TeV, over\n\n23 fb−1 at\n√\n\ns = 8 TeV, and 163 fb−1 at\n√\n\ns = 13 TeV. With these data CMS has conducted a broad\nphysics program leading perhaps most famously to the discovery of the Higgs boson [2].\n\nThe data collected by CMS are a unique scientific resource, impossible to produce again, and\nare therefore worthy of preservation. These data, and the results produced from their analysis,\ncomprise a large part of the scientific legacy of CMS. In recognition of this legacy, and in the\nbenefits of the data to public education and engagement, and to the larger research community,\nCMS has drafted, adopted, and carried out a data preservation and open access policy.\n\nData preservation describes the efforts to preserve the data as well as the conditions in which\nit can be analyzed and open access means making the data and conditions available and useable to\nthe public. Both concepts are interdependent as a dataset can’t be used (and reused) unless it and\nthe conditions for its use are preserved. Data used are data preserved. A comprehensive perspective\non open data and reproducibility may be found here [3].\n\nThis paper begins by describing the current fulfillment of the CMS data preservation and open\naccess policy. It continues with a discussion of the challenges faced and how they were tackled,\nwith specific examples. We end with a discussion of future plans.\n\n2. Open data policy and current releases\n\nData preservation and open access policy in CMS [4] includes a commitment to publish 50%\nof collision data after three years and after ten years to release up to 100%. The data are released\nunder an open license, the Creative Commons CC0 waiver [5], which essentially releases to the\npublic domain. There have been several previous releases of CMS collision data since the first in\n2014. The current releases of proton-proton collision data in terms of year, integrated luminosity,\nand center-of-mass energy are itemized below.\n\n• 2010: 32 pb−1 at\n√\n\ns = 7 TeV\n\n• 2011: 2.3 fb−1 at\n√\n\ns = 7 TeV and simulation\n\n• 2012: 11.6 fb−1 at\n√\n\ns = 8 TeV and simulation\n\nCMS and the other LHC experiments have adopted similar concepts of levels of access to\ndata. The lowest and simplest level is Level 1, which describes data directly related to publications\nsuch as original figures and values from plots and tables. Level 2 describes simplified data formats\nsuitable for education and outreach. The next level of complexity is Level 3, which describes\n“analysis level” reconstructed data and simulation and associated software. It is at this level that\nthe one would find the particle physicist conducting analysis. Finally, and at the highest level of\ncomplexity is Level 4, which describes raw, unreconstructed data and associated software. CMS\nopen data efforts mostly focus on Levels 2 and 3 although an initial Level 4 release is described\nhere.\n\n1\n\n\n\nP\no\nS\n(\nL\nH\nC\nP\n2\n0\n1\n9\n)\n2\n6\n0\n\nCMS Open Data Thomas McCauley\n\nAll open datasets, software, and documentation are available via the CERN Open Data Por-\ntal [6], a collaborative effort between the CERN Information Technology and CERN Scientific\nInformation Services teams and the experimental collaborations who release open data on it. The\nCERN Open Data Portal is built with the Invenio [7, 8] library management software framework.\nProducts such as datasets, software, and documentation are shared under open licenses and are is-\nsued digital object identifiers (DOIs) so that they are citeable. Backend data storage is provided by\nEOS [9].\n\n3. Challenges, solutions, and use-cases\n\nThere are several challenges that one must face in making such large and complex datasets\npublic and useable. The data do not exist on their own but are just one part of an analysis “ecosys-\ntem”. Therefore analysis software and workflows as well as metadata such as detector conditions\nand corrections need to be preserved and documented as well. Finally, a considerable amount of\ncombined physics and software knowledge is needed in order to make meaningful use of the data.\n\nThese challenges are met in several ways. A general solution is to provide for different levels\nof knowledge and expertise, from students up to professional researchers, roughly corresponding to\nthe levels of access described in the previous section. For students it is perhaps more appropriate to\nprovide simplified datasets, ones that are in less complex, easier-to-use formats and that also con-\ntained simplified information. An example would be a text-based format which lists the four-vector\ninformation for physics objects such as muons from already-selected events. Such files are used in\neducation programs such as masterclasses. At various levels of complexity example analysis code\nis available and is publicly accessible either on the CERN Open Data Portal or on a publicy acces-\nsible software repository like GitHub (in many cases on both). For more advanced users software\nenvironments such as virtual machines and containers are provided. In these environments one has\naccess to datasets, experiment software framework, and conditions. Finally, it should almost go\nwithout saying that comprehensive, clear, and maintained documentation is essential.\n\nA more detailed discussion of the current uses of CMS open data will illustrate further how\nthe challenges are met. The first and most enduring use of open data has been in the International\nMasterclasses [10], particle physics analysis exercises with literally global reach aimed at the high-\nschool level. The datasets used in these exercises are derived, simplified datasets such as those\nfound in “Event files for CMS masterclass exercise 2014.” [11]. In addition there is an active\neffort to create open source exercises in multiple languages using simplified formats analyzed using\nJupyter notebooks [12], interactive analysis environments run in a browser. These exercises have\nbeen used in teacher training in Finland [13] and have been partly used and developed in the course\nof the CERN High School Teacher program. More materials related to both programs may be\nfound on GitHub [14].\n\nCMS has also provided many analysis examples, including one [15] based on the discovery\nof the Higgs boson using data collected in 2011 and 2012. This is a well-documented analysis\navailable at different levels of complexity, from simply making the four-lepton invariant plot seen\nin Figure 1, up to replicating the original analysis.\n\nMore advanced users may wish to have full access to CMS open data in its native format\nalong with CMS software and conditions. In this case CMS has provided virtual machine images\n\n2\n\n\n\nP\no\nS\n(\nL\nH\nC\nP\n2\n0\n1\n9\n)\n2\n6\n0\n\nCMS Open Data Thomas McCauley\n\nFigure 1: Invariant mass plot of events with four muons passing the selections found in the “Higgs-to-four-\nlepton analysis example using 2011-2012 data.” [15]\n\ncontaining the appropriate and necessary software and conditions. From here, the user has a com-\nplete environment in which to analyze CMS data at the level of the physicist. A lighter-weight\nalternative to a full virtual machine image is a Docker [16] container in which one has a self-\ncontained environment that shares the kernel and resources of the host system on which it is run.\nDocker containers with the CMS analysis environment [17] are available from versioned public\nrepositories [18]. Among the benefits of using a container is that their use can aid in reusability\nand reproducibility. One can fetch environment and analysis code and run in one command; an\nexample can be found here [19].\n\nAdvanced users have made use of the data and tools for research purposes. This research\nincludes searches for new particles [20, 21], jet and QCD studies [22, 23], and machine learning\nstudies [24, 25, 26]. Furtherfore, and very usefully, some of the research users have provided valu-\nable and detailed feedback and perspectives [27, 28] on the challenges one faces when embarking\non research with CMS open data.\n\n4. New dataset releases and future plans\n\nThe latest release of data reflects the ever-growing application of machine learning (ML) tech-\nniques to challenges in high-energy physics and the increasing collaboration of physicists and the\ndata science and ML community [29]. The challenges for ML application in high-energy physics\ninclude particle identification, tracking and mitigation of pile-up.\n\nOne dataset [30] derived from Run 2 proton-proton simulation data at\n√\n\ns = 13 TeV focuses on\nthe problem of b-jet tagging in which the b quarks come from the decay of a Higgs boson, a channel\n\n3\n\n\n\nP\no\nS\n(\nL\nH\nC\nP\n2\n0\n1\n9\n)\n2\n6\n0\n\nCMS Open Data Thomas McCauley\n\nwhich suffers from a formidable QCD background. Further datasets are devoted to the challenges\nof top quark identification [31] and to studying the flavor content of jets [32]. Another dataset [33]\nis devoted to the challenge of particle tracking in the future era of high-luminosity collisions and is\nderived from simulations of collisions in the tracker after Phase 2 upgrades. All of the ML datasets\nare released with extensive documentation on their content, how to use them, and how to reproduce\nthem with modified content. More information on the state of ML datasets and their use can be\nfound in this contribution [34] to this conference.\n\nAs stated before, part of CMS’ data preservation and open access policy is to release 100% of\ndata within ten years of collection. As a fulfilment of this commitment, the remainder of proton-\nproton collision data at 7 TeV center-of-mass collected in 2010 are included in this new release.\nIn addition to these data, a small sample of raw data from each of the years 2010-2012 are also\nreleased. An example of which can be found here [35]. These samples will facilitate testing of the\nprocessing chain from raw to reconstructed data in the legacy environment and open the possibility\nfor future reconstruction algorithm studies and represent the first public release of Level 4 data.\n\nThe CASTOR calorimeter [36] operated in the very-forward (-6.6 < η < -5.2) region of CMS\nduring LHC Runs 1 and 2. Reconstructed data and simulation collected in 2019 at\n\n√\ns = 0.9 and\n\n7 TeV are included [37, 38, 39] in the new release, representing the first release of data exploring\nthis region of kinematic phase space.\n\nIn addition to data CMS has also prepared and released instructions and examples on how to\ngenerate simulated events and how to analyze data in a Docker container [40], within which one\nhas access to the CMS software environment and can analyse data. Finally there have also been\nimprovements in searching through the simulated data and to discover the provenance of datasets.\n\nFuture plans for open data on CMS include a continuation of release policy, continuous main-\ntenance and improvement of documentation and example analyses (responding always to valuable\nuser feedback), and to further exploit the educational potential of CMS data.\n\n5. Acknowledgements\n\nWe would like to thank the CERN Information Technology and CERN Scientific Information\nServices teams for providing resources and expertise to build and maintain the CERN Open Data\nPortal. We would also like to thank the organizers of the LHCP for a productive and informative\nconference.\n\nReferences\n\n[1] CMS Collaboration, JINST 3 S08004 (2008).\n\n[2] S. Chatrchyan et al. [CMS Collaboration], Phys. Lett. B 716 (2012) 30 [arXiv:1207.7235 [hep-ex]].\n\n[3] X. Chen et al., Nature Phys. 15 (2019) no.2, 113 [DOI:10.1038/s41567-018-0342-2].\n\n[4] CMS Collaboration (2018), CERN Open Data Portal [DOI:10.7483/OPENDATA.CMS.7347.JDWH].\n\n[5] https://creativecommons.org/publicdomain/zero/1.0/\n\n[6] https://opendata.cern.ch\n\n[7] J. Kuncar, L. H. Nielsen, T. Simko, http://urn.fi/URN:NBN:fi-fe2014070432236\n\n4\n\n\n\nP\no\nS\n(\nL\nH\nC\nP\n2\n0\n1\n9\n)\n2\n6\n0\n\nCMS Open Data Thomas McCauley\n\n[8] http://invenio-software.org\n\n[9] A. Peters et al., http://eos.web.cern.ch\n\n[10] https://physicsmasterclasses.org\n\n[11] T. McCauley (2014), CERN Open Data Portal [DOI:10.7483/OPENDATA.CMS.SSYF.EGXW].\n\n[12] https://jupyter.org\n\n[13] https://icps.helsinki.fi/myabstract/2019/full.php?sid=edu-10006\n\n[14] https://github.com/cms-opendata-education\n\n[15] N. Z. Jomhari, A. Geiser, A. A. Bin Anuar (2017), CERN Open Data Portal\n[DOI:10.7483/OPENDATA.CMS.JKB8.RR42].\n\n[16] https://docker.com\n\n[17] C. Lange, clelange/cmssw-docker: Release for Zenodo (Version v1.0). Zenodo.\nhttp://doi.org/10.5281/zenodo.3374808\n\n[18] https://hub.docker.com/u/cmsopendata\n\n[19] https://github.com/reanahub/reana-demo-cms-h4l\n\n[20] C. Cesarotti, Y. Soreq, M. J. Strassler, J. Thaler, W. Xue, Phys. Rev. D 100 (2019) no.1, 015021\n[arXiv:1902.04222 [hep-ph]].\n\n[21] C. G. Lester and M. Schott, [arXiv:1904.11195 [hep-ex]].\n\n[22] A. Tripathee, W. Xue, A. Larkoski, S. Marzani, J. Thaler, Phys. Rev. D 96 (2017) no.7, 074003\n[arXiv:1704.05842 [hep-ph]].\n\n[23] A. Larkoski, S. Marzani, J. Thaler, A. Tripathee, W. Xue, Phys. Rev. Lett. 119 (2017) no.13, 132003\n[arXiv:1704.05066 [hep-ph]].\n\n[24] C. F. Madrazo, I. H. Cacha, L. L. Iglesias, J. M. de Lucas, [arXiv:1708.07034 [cs.CV]].\n\n[25] M. Andrews, M. Paulini, S. Gleyzer, B. Poczos, [arXiv:1807.11916 [hep-ex]].\n\n[26] M. Andrews et al., [arXiv:1902.08276 [hep-ex]].\n\n[27] http://opendata.cern.ch/docs/cms-the-future-is-open-2017\n\n[28] M. Strassler, J. Thaler, Nature Physics 15 725 (2019).\nhttps://doi.org/10.1038/s41567-019-0628-z\n\n[29] K. Albertsson et al., J. Phys. Conf. Ser. 1085 (2018) no.2, 022008 [arXiv:1807.02876\n[physics.comp-ph]].\n\n[30] J. Duarte (2019), CERN Open Data Portal [DOI:10.7483/OPENDATA.CMS.JGJX.MS7Q].\n\n[31] A. Di Florio, F. Pantaleo, M. Pierini (2019), CERN Open Data Portal\n[DOI:10.7483/OPENDATA.CMS.N1IN.TQHD].\n\n[32] K. Kallonen (2019), CERN Open Data Portal [DOI:10.7483/OPENDATA.CMS.RY2V.T797].\n\n[33] E. Usai, M. Andrews, B. Burkle, S. Gleyzer, M. Narain (2019), CERN Open Data Portal\n[DOI:10.7483/OPENDATA.CMS.CHC3.5KPG].\n\n[34] J. Duarte on behalf of the CMS Collaboration, these proceedings.\n\n5\n\n\n\nP\no\nS\n(\nL\nH\nC\nP\n2\n0\n1\n9\n)\n2\n6\n0\n\nCMS Open Data Thomas McCauley\n\n[35] CMS Collaboration (2019), CERN Open Data Portal\n[DOI:10.7483/OPENDATA.CMS.W24L.SGYC].\n\n[36] P. Gunnellini [CMS Collaboration], [arXiv:1304.2943 [physics.ins-det]].\n\n[37] CMS Collaboration (2019), CERN Open Data Portal [DOI:10.7483/OPENDATA.CMS.QJ68.VK85].\n\n[38] CMS Collaboration (2019), CERN Open Data Portal\n[DOI:10.7483/OPENDATA.CMS.1R58.OMBD].\n\n[39] CMS Collaboration (2019), CERN Open Data Portal [DOI:10.7483/OPENDATA.CMS.HK0O.ZF9Q].\n\n[40] http://opendata.cern.ch/docs/cms-guide-docker\n\n6"},"fulltext":true,"source":"PoS","key":"e88661e562f4132d24e3bdbeaa049978","url":"https://inspirehep.net/files/e88661e562f4132d24e3bdbeaa049978"}],"citation_count":5,"citation_count_without_self_citations":4,"publication_info":[{"journal_volume":"LHCP2019","artid":"260","conference_record":{"$ref":"https://inspirehep.net/api/conferences/1642361"},"year":2019,"journal_record":{"$ref":"https://inspirehep.net/api/journals/1213080"},"page_start":"260","journal_title":"PoS","parent_record":{"$ref":"https://inspirehep.net/api/literature/1770236"},"cnum":"C19-05-20"}],"authors":[{"raw_affiliations":[{"value":"University of Notre Dame (US)"}],"full_name_unicode_normalized":"mccauley, thomas","last_name":"Mccauley","affiliations":[{"record":{"$ref":"https://inspirehep.net/api/institutions/903085"},"value":"Notre Dame U."}],"uuid":"3e295066-9a7b-4308-ac1a-49883a968750","affiliations_identifiers":[{"schema":"ROR","value":"https://ror.org/00mkhxb43"}],"full_name":"Mccauley, Thomas","record":{"$ref":"https://inspirehep.net/api/authors/998129"},"ids":[{"schema":"INSPIRE ID","value":"INSPIRE-00319706"},{"schema":"ORCID","value":"0000-0001-6589-8286"},{"schema":"INSPIRE BAI","value":"T.McCauley.1"}],"signature_block":"MCALYt","first_name":"Thomas","recid":998129}],"citeable":true,"copyright":[{"year":2019,"holder":"The Author(s)"}],"$schema":"https://inspirehep.net/schemas/records/hep.json","keywords":[{"schema":"INSPIRE","value":"talk: Puebla 2019/05/20"},{"schema":"INSPIRE","value":"p p: scattering"},{"schema":"INSPIRE","value":"p p: colliding beams"},{"schema":"INSPIRE","value":"CMS"},{"schema":"INSPIRE","value":"data preservation"},{"schema":"INSPIRE","value":"CERN LHC Coll"},{"schema":"INSPIRE","value":"programming"},{"schema":"INSPIRE","value":"data management"}],"references":[{"reference":{"collaborations":["CMS Collaboration"],"label":"1","publication_info":{"journal_volume":"3","artid":"S08004","year":2008,"journal_title":"JINST"}},"curated_relation":false,"record":{"$ref":"https://inspirehep.net/api/literature/796887"}},{"reference":{"collaborations":["CMS Collaboration"],"arxiv_eprint":"1207.7235","label":"2","publication_info":{"journal_volume":"716","artid":"30","year":2012,"page_start":"30","journal_title":"Phys.Lett.B"},"authors":[{"full_name":"Chatrchyan, S."}]},"curated_relation":false,"record":{"$ref":"https://inspirehep.net/api/literature/1124338"}},{"reference":{"dois":["10.1038/s41567-018-0342-2"],"label":"3","publication_info":{"journal_volume":"15","artid":"113","year":2019,"page_start":"113","journal_title":"Nature Phys."},"authors":[{"full_name":"Chen, X."}]},"curated_relation":false,"record":{"$ref":"https://inspirehep.net/api/literature/1704097"}},{"reference":{"collaborations":["CMS Collaboration"],"dois":["10.7483/OPENDATA.CMS.7347.JDWH"],"label":"4","publication_info":{"year":2018},"misc":["CERN Open Data Portal"]}},{"reference":{"urls":[{"value":"https://creativecommons.org/publicdomain/zero/1.0/"}],"label":"5"}},{"reference":{"urls":[{"value":"https://opendata.cern.ch"}],"label":"6"}},{"reference":{"urls":[{"value":"http://urn.fi/URN"}],"label":"7","misc":[":NBN:fi-fe2014070432236"],"authors":[{"full_name":"Kuncar, J."},{"full_name":"Nielsen, L.H."},{"full_name":"Simko, T."}]}},{"reference":{"urls":[{"value":"http://invenio-software.org"}],"label":"8"}},{"reference":{"urls":[{"value":"http://eos.web.cern.ch"}],"label":"9","authors":[{"full_name":"Peters, A."}]}},{"reference":{"urls":[{"value":"https://physicsmasterclasses.org"}],"label":"10"}},{"reference":{"dois":["10.7483/OPENDATA.CMS.SSYF.EGXW"],"label":"11","publication_info":{"year":2014},"misc":["CERN Open Data Portal"],"authors":[{"full_name":"McCauley, T."}]}},{"reference":{"urls":[{"value":"https://jupyter.org"}],"label":"12"}},{"reference":{"urls":[{"value":"https://icps.helsinki.fi/myabstract/2019/full.php?sid=edu-10006"}],"label":"13"}},{"reference":{"urls":[{"value":"https://github.com/cms-opendata-education"}],"label":"14"}},{"reference":{"dois":["10.7483/OPENDATA.CMS.JKB8.RR42"],"label":"15","publication_info":{"year":2017},"misc":["CERN Open Data Portal"],"authors":[{"full_name":"Jomhari, N.Z."},{"full_name":"Geiser, A."},{"full_name":"Bin Anuar, A.A."}]}},{"reference":{"urls":[{"value":"https://docker.com"}],"label":"16"}},{"reference":{"dois":["10.5281/zenodo.3374808"],"label":"17","misc":["clelange/cmssw-docker: Release for Zenodo (Version v1.0). 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Data preservation describes the efforts to not only preserve the data itself but the conditionsin which it can be analyzed. This requires archiving and documenting information such as runenvironment and conditions and tools such as analysis software and workflows. Open access describesthe efforts to share these tools and information with the public, including educators and researchers.These efforts present many challenges, partly due to the complexity and size of the CMS datasetsand also to the knowledge required to make meaningful use of them. We describe here how we dealtwith these challenges and the usage of CMS open data not only in public education and engagementbut also in fundamental research. 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The data was obtained and processed from the CERN Open Data Portal. For the total multiplicity component, it was found that a weighted superposition of a Negative Binomial Distribution (NBD) and a Furry–Yule Distribution (FYD) is able to describe the shoulder-like structure characteristic of Koba–Nielsen–Olesen (KNO) scaling violation well. The mean cluster size produced from collisions was also found to increase with collision energy. A prediction is given for pp collisions at s = 14 TeV.","abstract_source_suggest":{"input":"WSP"}},{"source":"arXiv","value":"A Chou-Yang type multiplicity distribution comprising a total multiplicity component and a binomial asymmetry component is used to describe charged hadron multiplicity data at $\\sqrt{s}=$ 0.9, 7 and 8 TeV from the CMS experiment at CERN. The data was obtained and processed from the CERN Open Data Portal. For the total multiplicity component, it was found that a convex sum of a Negative Binomial Distribution and a Furry-Yule Distribution is able to describe the shoulder-like structure characteristic of KNO scaling violation well. The mean cluster size produced from collisions was also found to increase with collision energy. A prediction is given for $pp$ collisions at $\\sqrt{s}=$ 14 TeV.","abstract_source_suggest":{"input":"arXiv"}}],"primary_arxiv_category":["hep-ph"],"titles":[{"source":"WSP","title":"Forward–backward multiplicity distribution with the Chou–Yang model for pp collisions at s = 0.9, 7 and 8 TeV from the CMS experiment"},{"source":"arXiv","title":"Forward-backward multiplicity distribution with the Chou-Yang model for $pp$ collisions at $\\sqrt{s}=$ 0.9, 7 and 8 TeV from the CMS experiment"}],"imprints":[{"date":"2023-05-05"}],"curated":true}},{"created":"2016-01-07T00:00:00+00:00","updated":"2025-02-05T12:49:26.877550+00:00","id":"1413825","links":{"bibtex":"https://inspirehep.net/api/literature/1413825?format=bibtex","latex-eu":"https://inspirehep.net/api/literature/1413825?format=latex-eu","latex-us":"https://inspirehep.net/api/literature/1413825?format=latex-us","json":"https://inspirehep.net/api/literature/1413825?format=json","json-expanded":"https://inspirehep.net/api/literature/1413825?format=json-expanded","cv":"https://inspirehep.net/api/literature/1413825?format=cv","citations":"https://inspirehep.net/api/literature/?q=refersto%3Arecid%3A1413825"},"metadata":{"documents":[{"filename":"10.1088_1742-6596_664_3_032027.pdf","description":"Fulltext from Publisher","key":"41d23627cf20a108daf44235cf4ca247","url":"https://inspirehep.net/files/41d23627cf20a108daf44235cf4ca247"},{"filename":"jpconf15_664_032027.pdf","attachment":{"content":"Open access to high-level data and analysis tools in\n\nthe CMS experiment at the LHC\n\nA Calderon1, D Colling2, A Huffman2, K Lassila-Perini3, T\nMcCauley4, A Rao5, A Rodriguez-Marrero1 and E Sexton-Kennedy6\n\n1 IFCA, CSIC-Univ. de Cantabria, Santander, Spain\n2 Department of Physics, Imperial College, London, UK\n3 Helsinki Institute of Physics, Helsinki, Finland\n4 University of Notre Dame, Notre Dame, IN, USA\n5 University of the West of England, Bristol, UK\n6 Fermi National Accelerator Laboratory, Batavia, IL, USA\n\nE-mail: k.lassila-perini@cern.ch\n\nAbstract. The CMS experiment, in recognition of its commitment to data preservation and\nopen access as well as to education and outreach, has made its first public release of high-level\ndata under the CC0 waiver: up to half of the proton-proton collision data (by volume) at 7\nTeV from 2010 in CMS Analysis Object Data format. CMS has prepared, in collaboration\nwith CERN and the other LHC experiments, an open-data web portal based on Invenio. The\nportal provides access to CMS public data as well as to analysis tools and documentation for the\npublic. The tools include an event display and histogram application that run in the browser. In\naddition a virtual machine containing a CMS software environment along with XRootD access\nto the data is available. Within the virtual machine the public can analyse CMS data; example\ncode is provided. We describe the accompanying tools and documentation and discuss the first\nexperiences of data use.\n\n1. Introduction\nThe Compact Muon Solenoid or CMS [1] is one of two general-purpose experiments at the Large\nHadron Collider (LHC) at CERN. Since 2010 CMS has collected around 28 fb−1 of proton-\nproton collision data at center-of-mass energies up to 8 TeV as well as data from proton-lead\nand lead-lead collisions. Analysis of these data have produced almost 400 published papers\ndescribing searches for new physics phenomena, measurements of known processes, as well as\ndiscovery of the Higgs boson [2].\n\nIn recognition of the importance of data preservation, re-use, and open access, CMS has\napproved a policy [3] that defines the collaboration’s approach. The policy covers many levels,\nfrom a commitment to publication in open-access journals, to release of reconstructed data,\nto the preservation and release of software and documentation needed for reconstruction and\nanalysis. In accordance with this policy, CMS has released, simultaneously with the release of\nthe CERN Open Data Portal [4] in November 2014, a first large set of reconstructed data for\npublic use. This dataset is around 27 TB of 2010 proton-proton collision data at 7 TeV.\n\n21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015) IOP Publishing\nJournal of Physics: Conference Series 664 (2015) 032027 doi:10.1088/1742-6596/664/3/032027\n\nContent from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution\nof this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.\n\nPublished under licence by IOP Publishing Ltd 1\n\n\n\n2. CMS open data release\nThe newly released data are in CMS Analysis Object Data (AOD) format [5], which is the\nformat used by CMS physicists for most analyses of CMS data in Run 1 (2010-2012). The data\nwere collected during 2010’s “RunB”, the latter of the two data-taking periods of the year, from\nmid-September to the end of October 2010, and corresponding to an integrated luminosity of\napproximately 38 pb−1. These high-level data are divided into so-called primary datasets, to\nwhich the data are directed based on the online event selection results. The dataset names, such\nas MinimumBias, Mu, Electron, and MultiJet (to give a few examples), reflect the selection\ncriteria.\n\nThe released data were reconstructed in 2011 with CMS software (CMSSW) [6] version 4.2\nrunning on Scientific Linux CERN 5 [7]. This is the latest complete reconstruction of the\ndata taken in 2010. The data were not specifically prepared for the public release. Therefore,\nacknowledging the difficulty of using these data and in order to ease further re-use, CMS\nhas provided, together with the analysis software in a virtual machine (VM), a basic set of\ninstructions on the usage of the data as well as the necessary condition database needed by the\nsoftware, along with a list of validated runs.\n\nWhile the release of the corresponding simulated Monte Carlo datasets is foreseen for future\nreleases of CMS data, it was not done for this first release, because no complete set of simulated\ndata exists for this version of CMSSW (4.2). The simulated datasets for 2010 were only available\nfor earlier CMSSW releases and their release as such was not considered useful. For the data\ntaken in 2011 and 2012, part of which are to be made publicly available at the next data release,\nthe simulated datasets are available with the same CMSSW release as the collision data, and\nwill be released together with the data.\n\nIn addition to the primary datasets, CMS provides some examples of further reprocessed\ndata derived from the primary datasets. These derived datasets are meant to be used with\nCMS analysis software (where reprocessing reduced the time needed for the final analysis) or\nwith online web applications (where reprocessing reduced the complexity of the data in terms\nof content and format). A reduction of the Mu [8] and Electron [9] primary datasets is provided\nas derived data [10, 11]. These datasets contain only electron and muon candidates respectively,\nwhich are extracted from the AOD files with standard CMS software tools, and they can be\nanalysed with an example code available with the open data release.\n\nThe derived datasets include an earlier release of a small, selected amount of data for use in\neducation and outreach. These datasets were reduced to the level of four-vectors and contain\nJ/ψ,Υ,W, and Z candidates as well as general two-muon and two-electron events. In order\nto make usage as straight-forward and simple as possible the data were released in human-\nreadable, text-based formats such as CSV (comma-separated variable) and JSON (JavaScript\nObject Notation) and have formed the core of the successful International Physics Masterclass\nprogram aimed at high-school students around the world [12].\n\nAll of the open data provided by CMS are released under the CC0 waiver [13], thus liberating\nthe data into the public domain. There are no restrictions on non-CMS collaborators in preparing\npublications based on the published data. Each dataset stored on the CERN Open Data\nPortal, described below, are also assigned Digital Object Identifiers (DOIs), which make the\ndatasets citable objects (like in this very paper). We do expect authors to use this citation when\npublishing papers based on CMS data [14] .\n\n3. CERN Open Data Portal\nCERN, in collaboration with CMS and the other LHC experiments, has prepared an open\ndata portal [4] from which the data and tools for the public are available. The portal itself is\nbuilt using CERN’s Invenio digital library software [15] as its base. Invenio provides document\norganization, search capability, and handling of metadata. CMS relies on CERN support and\n\n21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015) IOP Publishing\nJournal of Physics: Conference Series 664 (2015) 032027 doi:10.1088/1742-6596/664/3/032027\n\n2\n\n\n\nservices provided through the portal for legacy data storage, access to and distribution of the\ndata, and security and bandwidth restrictions for public access.\n\nThe portal builds upon the previous successes of public release of data for education and\noutreach but goes further by including the possibility for more in-depth, complex analyses, with\nthe high-level data now made public by CMS. The portal is therefore divided into two sections –\n“Education” and “Research” – to which the material is assigned depending on its potential use\nand “degree of difficulty”. The main entry view is shown in Figure 1. This division is designed to\nease the access to material of interest depending on the user’s intended usage. The portal directs\nthe user to simplified web applications on the “Education” side and to detailed instructions on\nhow to get started with analyses using CMS data on the “Research” side. The material is\nalso ordered into different collections. Contributions from the four major LHC experiments –\nALICE, ATLAS, CMS, and LHCb – are included.\n\n  \n\nFigure 1. The main entry to the CERN Open Data Portal.\n\nFor CMS the main collections are currently:\n\n• CMS Primary Datasets\n\n• CMS Derived Datasets\n\n• CMS Tools\n\n• CMS Open Data Instructions\n\n• CMS Learning resources.\n\nThe two first collections contain the data as described in the previous section. Each primary\ndataset record includes metadata fields including information of the provenance of the data\nand instructions for its usage. Records in the data collections may point to a record in the\ntools collections containing the software, which either can be used to study the data (in case of\nprimary datasets) or has been used to generate the data (in case of derived datasets).\n\nThe software records in the tools collections can also act as examples, from which external\nusers can build and extend their own applications. The open-source examples include code for\nimplementing a simple selection of dimuons from the Mu sample [8] and generating the output of\nthe four-vector information to a CSV file [16], and code for conversion of events into the format\nsuitable for viewing in the event display described in the next section. Also included is code for\n\n21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015) IOP Publishing\nJournal of Physics: Conference Series 664 (2015) 032027 doi:10.1088/1742-6596/664/3/032027\n\n3\n\n\n\ndoing a simple analysis of Z decays to two leptons and ZZ decays to four leptons [17]; the best\nZ candidates are selected by performing different quality selections on the leptons (muons and\nelectrons) and pairing those of high transverse momentum and opposite charge.\n\nThe tools collection also provides the source code for different web applications, described\nin the following section, that run on the derived datasets available from the portal, and the\ndownloadable VM image built on CernVM [18] containing the CMS software and computing\nenvironment with which the primary datasets can be accessed without direct download (served\nvia XRootD [19]).\n\nThe instructions collection pulls together the different tutorial and instruction pages of the\nportal. The learning resources collection links to various external resources that already use\nCMS open data and are readily usable in an educational context (teaching or learning).\n\n4. Web applications\nTwo applications using the open data are available for use within the portal: an event display\nand a histogram application. Both are client-side applications running in the browser.\n\n4.1. Event display\nThe event display [20] is an adaptation of an application [21] used by the public in educational\nprograms such as the International Physics Masterclasses [12]. This particular version of the\ndisplay is written in JavaScript, HTML, and CSS. The 3D rendering is canvas-based and is done\nusing the pre3d library [22]. A screenshot of an event in the display can be seen in Figure 2.\n\nFigure 2. A screenshot of an event display from the Mu primary dataset.\n\nThe input file format for the display is the ig format [23], which is a zip archive containing\na JSON file for each event. In Invenio, a record that contains a file in this format can be\nviewed in the browser with the invenio-previewer-ispy [24]. In the event display all of the ig files\navailable in the portal as records are available for viewing. There are fourteen CMS primary\n\n21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015) IOP Publishing\nJournal of Physics: Conference Series 664 (2015) 032027 doi:10.1088/1742-6596/664/3/032027\n\n4\n\n\n\ndatasets currently available on the portal; twenty-five events from each of the primary datasets\nare available in ig format for viewing. In addition to example events from the primary datasets,\nadditional events from previously-released datasets are available: these include di-lepton and W\nevents used in the masterclasses.\n\nSince the release of the portal, a new version of the display has been developed [25] that\nuses bootstrap.js for the user interface and WebGL for 3D rendering (defaulting to canvas-based\nrendering when WebGL is not available). This version will eventually replace the canvas-based\nversion currently available.\n\n4.2. Histogram tool\nA subset of events from several of the open datasets are available in CSV format, where each\nline contains event information at the level of four-vectors.\n\nThe information contained in these CSV files is available for view in an interactive histogram\napplication [26]. The application uses d3 [27] for creation of the histogram information and the\njQuery-based library Flot [28] for the graphics. An example of histograms for events where a\nW boson decays into an electron and a neutrino can be seen in Figure 3. Current functionality\nincludes logarithmic axes, range selection along the horizontal axis, and the ability to change\nthe bin width. In the future one will be able to, for multiple plots, make correlated selection of\nparameter ranges.\n\nFigure 3. A screenshot of the histogram tool in the open data portal.\n\n5. Communication and outreach\nInstead of adopting the “build it and they will come” approach, it was decided from the\nstart that appropriate means of dissemination would need to be exercised in order to inform\ninterested parties about the existence of the CERN Open Data Portal with CMS open data,\nand engage with said parties across the globe. Various audiences were considered, including the\npress, educators (school and university), physicists, as well as members of the general public.\nAccordingly, communications and outreach efforts were handled as described below.\n\n5.1. Formal communications: CERN press release\nA press release was drafted by all involved partners and issued by CERN in November 2014 [29]\nto announce the launch of the CERN Open Data Portal to the widest possible audience. CMS\n\n21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015) IOP Publishing\nJournal of Physics: Conference Series 664 (2015) 032027 doi:10.1088/1742-6596/664/3/032027\n\n5\n\n\n\nalso drafted an official statement [30], which was linked from the CERN press release, covering\nthe collaboration’s motivations for releasing the data and the expected audiences who would\nuse them. The press release generated worldwide coverage, and the launch of the portal was\ncovered in several media publications. CERN’s official tweet [31] announcing the portal was\nthe third-most re-tweeted post of 2014, which showed that there was interest among the wider\ncommunity of science enthusiasts.\n\n5.2. Engagement through social media\nTo leverage this wider appeal of CMS open data, the CMS Communications Group organized\nan “Ask Me Anything” or AMA session on the social platform reddit [32]. Participants included\nrepresentatives of CMS (including three of the authors of this document), CERN IT and the\nCERN Library team. The AMA session presented an opportunity not only to discuss our views\non open science as well as open data and data preservation but also to answer questions that the\npublic may have about the data release. It was also an occasion to informally highlight that we,\nthe data providers, are aware that it isn’t easy to use the data and, while we do not have many\nresources to help with projects developed on top of them, we still think releasing real LHC data\nis an exercise worth doing.\n\nThis engagement session proved to be very successful: in quantative terms, compared with\nthe regular traffic, there was a five-fold increase in hits to the CERN Open Data Portal following\nthe AMA [33].\n\n5.3. Contacting the team\nAlthough limited human resources prevent large-scale support for users, it was felt that a\nmeans for contacting the core team behind the CERN Open Data Portal (including CMS\nrepresentatives) should be available, with support provided on a best-effort basis. Therefore, a\nmailing list (opendata-support@cern.ch) with core members was set up and advertised via the\nportal itself.\n\nA variety of people have contacted the mailing list not only presenting ideas and feedback\nfor enhancing the portal (particularly on the usability/accessibility front) but have also wishing\nto collaborate on the project.\n\n6. Usage\nThe CERN Open Data Portal was visited by 82000 distinct users during the month after the\nlaunch. Of these users, around 600 downloaded data files over HTTP, 5000 read the “About”\npages, 21000 viewed the collections, 16000 used the event display, 3000 used the histogram\napplication, 21000 viewed the records, and 10000 used the built-in search. The dataset download\nwas estimated in January 2015, and there were around 1000 access hits through XRootD from\nthe VMs and around 200 direct downloads from the portal [33].\n\n7. Impact and outook\nThe CMS data release has fostered collaboration and enabled common solutions in data\npreservation. It has been the primus motor for the CERN Open Data Portal and opened a\nway for development of further services in the domain, which are now being prototyped. The\npioneering work required to define metadata for complex records such as primary and derived\ndatasets and the heterogeneous items in the tools collection has been an excellent starting point\nfor further development in the area. The release has also encouraged other HEP experiment to\ndiscuss and decide their own data release conditions.\n\nThe impact of the data release has been, in general, very positive. The CMS Collaboration\nis now evaluating the release process, and, if there is no unexpected negative impact to the\ncollaboration, the data releases will become a regular procedure.\n\n21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015) IOP Publishing\nJournal of Physics: Conference Series 664 (2015) 032027 doi:10.1088/1742-6596/664/3/032027\n\n6\n\n\n\nAcknowledgments\nThe authors and the CMS Collaboration are grateful for the support we have received from\nCERN-IT and the Invenio team, CERN-GS-SIS (the library team), CERN-PH-SFT (for the\nCMS-specific CernVM image), the DPHEP collaboration, as well as the US NSF and DOE and\nthe QuarkNet collaboration.\n\nReferences\n[1] CMS Collaboration 2008 The CMS experiment at the CERN LHC JINST 3 S08004 http://cern.ch/cms\n\n[2] CMS Collaboration 2012 Observation of a new boson at a mass of 125 GeV with the CMS experiment at the\nLHC Phys. Lett. B 716 30\n\n[3] Lassila-Perini K et al. 2014 Implementing the data preservation and open access policy in CMS\nJ.Phys.Conf.Ser. 513 042029\n\n[4] http://opendata.cern.ch\n\n[5] Hinzmann A 2011 Tools for Physics Analysis in CMS J.Phys.Conf.Ser. 331 032042\n[6] http://cms-sw.github.io/index.html\n\n[7] http://linux.web.cern.ch/linux/scientific5/\n\n[8] CMS collaboration (2014). Mu primary dataset in AOD format from RunB of 2010 (/Mu/Run2010B-\nApr21ReReco-v1/AOD). CERN Open Data Portal. DOI: 10.7483/OPENDATA.CMS.B8MR.C4A2\n\n[9] CMS collaboration (2014). Electron primary dataset in AOD format from RunB of 2010 (/Electron/Run2010B-\nApr21ReReco-v1/AOD). CERN Open Data Portal. DOI: 10.7483/OPENDATA.CMS.PDY4.7H2H\n\n[10] Rodriguez Marrero, A. (2014). Muons and electrons in PAT candidate format derived from /Mu/Run-\n2010B-Apr21ReReco-v1/AOD primary dataset. CERN Open Data Portal. DOI: 10.7483/OPEN-\nDATA.CMS.RJW2.QP44\n\n[11] Rodriguez Marrero, A. (2014). Muons and electrons in PAT candidate format derived from /Electron/Run-\n2010B-Apr21ReReco-v1/AOD primary dataset. CERN Open Data Portal. DOI: 10.7483/OPEN-\nDATA.CMS.HHTK.9FS2\n\n[12] Cecire K et al. 2014 The CMS Masterclass and Particle Physics Outreach EPJ Web Conf. 71 00027\nhttp://www.physicsmasterclasses.org\n\n[13] http://creativecommons.org/publicdomain/zero/1.0\n\n[14] Disclaimer section of http://opendata.cern.ch/about/CMS\n\n[15] http://invenio-software.org, http://urn.fi/URN:NBN:fi-fe2014070432236\n[16] McCauley T 2014 Software to extract data in csv format from a CMS primary dataset CERN Open Data\n\nPortal DOI: 10.7483/OPENDATA.CMS.RB4W.3ZK9\n[17] Rodriguez Marrero A 2014 Two-lepton/four-lepton analysis example CERN Open Data Portal DOI:\n\n10.7483/OPENDATA.CMS.QXY9.X47P\n[18] http://cernvm.cern.ch\n\n[19] http://xrootd.org\n\n[20] http://opendata.cern.ch/visualise/events/CMS\n\n[21] Hategan M, McCauley T and Nguyen P 2012 A browser-based event display for the CMS experiment at the\nLHC J.Phys.Conf.Ser. 396 022022\n\n[22] https://github.com/deanm/pre3d\n\n[23] Alverson G et al. 2012 iSpy: a powerful and lightweight event display J. Phys. Conf. Ser. 396 022002\n[24] https://github.com/inveniosoftware/invenio-previewer-ispy\n\n[25] https://github.com/cms-outreach/ispy-webgl\n\n[26] http://opendata.cern.ch/visualise/histograms/CMS\n\n[27] http://d3js.org/\n\n[28] http://www.flotcharts.org/\n\n[29] http://press.web.cern.ch/press-releases/2014/11/cern-makes-public-first-data-lhc-experiments\n\n[30] http://cms.web.cern.ch/news/cms-releases-first-batch-high-level-lhc-open-data\n\n[31] https://twitter.com/cern/status/535443404755566592\n\n[32] http://redd.it/2nxwkb\n\n[33] T. Simko, private communication\n\n21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015) IOP Publishing\nJournal of Physics: Conference Series 664 (2015) 032027 doi:10.1088/1742-6596/664/3/032027\n\n7"},"fulltext":true,"key":"ab72f72e0b34dbc10b5add223d09facb","url":"https://inspirehep.net/files/ab72f72e0b34dbc10b5add223d09facb"}],"citation_count":2,"citation_count_without_self_citations":2,"report_numbers":[{"value":"FERMILAB-CONF-16-081-CD"}],"publication_info":[{"journal_volume":"664","artid":"032027","conference_record":{"$ref":"https://inspirehep.net/api/conferences/1320679"},"year":2015,"journal_record":{"$ref":"https://inspirehep.net/api/journals/1212279"},"journal_issue":"3","journal_title":"J.Phys.Conf.Ser.","parent_record":{"$ref":"https://inspirehep.net/api/literature/1412138"},"cnum":"C15-04-13"}],"authors":[{"raw_affiliations":[{"value":"IFCA - CSIC-Univ. de Cantabria - Santander - Spain"}],"full_name_unicode_normalized":"calderon, a.","full_name":"Calderon, A.","curated_relation":true,"record":{"$ref":"https://inspirehep.net/api/authors/1064395"},"affiliations":[{"record":{"$ref":"https://inspirehep.net/api/institutions/903772"},"value":"Cantabria U., Santander"}],"last_name":"Calderon","ids":[{"schema":"ORCID","value":"0000-0002-7205-2040"},{"schema":"INSPIRE BAI","value":"Alicia.Calderon.1"}],"signature_block":"CALDARANa","uuid":"490a4ee6-5c87-438e-b02e-b43da97c46bb","first_name":"A.","recid":1064395},{"affiliations_identifiers":[{"schema":"ROR","value":"https://ror.org/041kmwe10"}],"raw_affiliations":[{"value":"Department of Physics - Imperial College - London - UK"}],"full_name_unicode_normalized":"colling, d.","full_name":"Colling, D.","record":{"$ref":"https://inspirehep.net/api/authors/1013227"},"affiliations":[{"record":{"$ref":"https://inspirehep.net/api/institutions/902868"},"value":"Imperial Coll., London"}],"last_name":"Colling","ids":[{"schema":"INSPIRE BAI","value":"D.J.Colling.1"}],"signature_block":"CALANGd","uuid":"79310e1c-b7eb-434d-add0-4acd731ddd5d","first_name":"D.","recid":1013227},{"affiliations_identifiers":[{"schema":"ROR","value":"https://ror.org/041kmwe10"}],"raw_affiliations":[{"value":"Department of Physics - Imperial College - London - UK"}],"full_name_unicode_normalized":"huffman, a.","full_name":"Huffman, A.","record":{"$ref":"https://inspirehep.net/api/authors/2270967"},"affiliations":[{"record":{"$ref":"https://inspirehep.net/api/institutions/902868"},"value":"Imperial Coll., London"}],"last_name":"Huffman","ids":[{"schema":"INSPIRE BAI","value":"A.Huffman.1"}],"signature_block":"HAFNANa","uuid":"f06dee99-9eae-410c-9d8a-c1ef4c6caf63","first_name":"A.","recid":2270967},{"raw_affiliations":[{"value":"Helsinki Institute of Physics - 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diagram exemplifying the production and exotic decay of the SM-like Higgs boson into a pair of pseudoscalars that subsequently decay into a $\\mu^{-}\\mu^{+}$ and $c\\bar{c}$ pair respectively (left). Besides it, there is a schematic representation of the final state topology (right), where the effects of the boosting acquired by the pair of light bosons are illustrated.","label":"fig:Feynman-Diagram-and-Topology","source":"arxiv","key":"12f9106382903eddfae160a8fa9c62c4","url":"https://inspirehep.net/files/12f9106382903eddfae160a8fa9c62c4"},{"filename":"Decay_HToaa.png","material":"preprint","caption":"Feynman diagram exemplifying the production and exotic decay of the SM-like Higgs boson into a pair of pseudoscalars that subsequently decay into a $\\mu^{-}\\mu^{+}$ and $c\\bar{c}$ pair respectively (left). Besides it, there is a schematic representation of the final state topology (right), where the effects of the boosting acquired by the pair of light bosons are illustrated.","label":"fig:Feynman-Diagram-and-Topology","source":"arxiv","key":"7a24bf5f156f8bb01c476f3fec06e475","url":"https://inspirehep.net/files/7a24bf5f156f8bb01c476f3fec06e475"},{"filename":"MuMu_DeltaR.png","material":"preprint","caption":"Relevant distributions of studies performed at generator level. On the left, the distribution of the angular separation ($\\Delta R$) between the two muons that belong to the $\\text{a} \\rightarrow \\mu^{-}\\mu^{+}$ decay. On the right, the distribution of the two c-tagging discriminators (C-vs-L and C-vs-B) that are obtained for the DeepJet algorithm applied on the AK4 reconstructed jet that matches the $\\text{a} \\rightarrow c\\bar{c}$ candidate. This matching was performed imposing that the angular distance between the direction of the light boson (truth level) and the direction of the AK4 jet (reconstructed level) is $\\Delta R < 0.4$. The distributions are normalized to unity and are illustrated for four representative mass points: $\\ma = 5 \\GeV$ (purple line), $\\ma = 7 \\GeV$ (blue line), $\\ma = 9 \\GeV$ (yellow line), and $\\ma = 11 \\GeV$ (red line).","label":"fig:GenLevel-Studies","source":"arxiv","key":"4efe51005dd6484c3d4b38c46e093d73","url":"https://inspirehep.net/files/4efe51005dd6484c3d4b38c46e093d73"},{"filename":"MatchGenJet_C-tagging.png","material":"preprint","caption":"Relevant distributions of studies performed at generator level. On the left, the distribution of the angular separation ($\\Delta R$) between the two muons that belong to the $\\text{a} \\rightarrow \\mu^{-}\\mu^{+}$ decay. On the right, the distribution of the two c-tagging discriminators (C-vs-L and C-vs-B) that are obtained for the DeepJet algorithm applied on the AK4 reconstructed jet that matches the $\\text{a} \\rightarrow c\\bar{c}$ candidate. This matching was performed imposing that the angular distance between the direction of the light boson (truth level) and the direction of the AK4 jet (reconstructed level) is $\\Delta R < 0.4$. The distributions are normalized to unity and are illustrated for four representative mass points: $\\ma = 5 \\GeV$ (purple line), $\\ma = 7 \\GeV$ (blue line), $\\ma = 9 \\GeV$ (yellow line), and $\\ma = 11 \\GeV$ (red line).","label":"fig:GenLevel-Studies","source":"arxiv","key":"7e1bb0f2dd2e125dc57ccb13dcdb77cb","url":"https://inspirehep.net/files/7e1bb0f2dd2e125dc57ccb13dcdb77cb"},{"filename":"CTag_CvsL_ROCs.png","material":"preprint","caption":"ROC curves exhibiting the discrimination capability of the C-vs-L (left) and C-vs-B (right) discriminators for the DeepJet algorithm using AK4 jets with similar requirements to the ones utilized in this analysis. The performance of these two discriminators when operating on signal $c\\bar{c}$ jets ($\\text{a} \\rightarrow c\\bar{c}$ jets) is compared to the performance on regular QCD c jets. For the signal process, only AK4 jets that have been matched by angular distance ($\\Delta R < 0.4$) to the direction of flight of the light boson are considered true $c\\bar{c}$ jets. For the estimation of the mistagging rate, regular QCD light (udsg) and b jets have been employed. Curves with dashed lines are shown for two representative mass hypotheses of the signal process: $\\ma = 5 \\GeV$ (purple line) and $\\ma = 11 \\GeV$ (red line). The black solid line has been derived using simulated jets originating from the QCD multi-jet process.","label":"fig:CTag-Discrimination","source":"arxiv","key":"cf4a364a72fc58cc00d68a1171394800","url":"https://inspirehep.net/files/cf4a364a72fc58cc00d68a1171394800"},{"filename":"CTag_CvsB_ROCs.png","material":"preprint","caption":"ROC curves exhibiting the discrimination capability of the C-vs-L (left) and C-vs-B (right) discriminators for the DeepJet algorithm using AK4 jets with similar requirements to the ones utilized in this analysis. The performance of these two discriminators when operating on signal $c\\bar{c}$ jets ($\\text{a} \\rightarrow c\\bar{c}$ jets) is compared to the performance on regular QCD c jets. For the signal process, only AK4 jets that have been matched by angular distance ($\\Delta R < 0.4$) to the direction of flight of the light boson are considered true $c\\bar{c}$ jets. For the estimation of the mistagging rate, regular QCD light (udsg) and b jets have been employed. Curves with dashed lines are shown for two representative mass hypotheses of the signal process: $\\ma = 5 \\GeV$ (purple line) and $\\ma = 11 \\GeV$ (red line). The black solid line has been derived using simulated jets originating from the QCD multi-jet process.","label":"fig:CTag-Discrimination","source":"arxiv","key":"ca665c2e8dc601d4f4666fa3674fbed6","url":"https://inspirehep.net/files/ca665c2e8dc601d4f4666fa3674fbed6"},{"filename":"Fitting_Signal_Ma-5.png","material":"preprint","caption":"Graphic representation of the unbinned maximum likelihood fit performed to simulated events using the signal model underlined by equation~\\ref{eq:crystal-ball} and described in the text. Two representative signal mass hypotheses, namely $\\ma = 5 \\GeV$ (left) and $\\ma = 9 \\GeV$ (right), are shown for the ggF process. The data points represent the reconstructed di-muon invariant mass distribution as obtained from simulated signal events, whereas the solid colored line represents the analytical shape of the signal after a fit was performed. The lower panel shows the standard pull obtained when comparing the fitted p.d.f. to the simulated data. The error bars on the data points include only the uncertainty related to the simulation statistics.","label":"fig:Signal-Model","source":"arxiv","key":"95014a10cebd6bd9353c5466553445a1","url":"https://inspirehep.net/files/95014a10cebd6bd9353c5466553445a1"},{"filename":"Fitting_Signal_Ma-9.png","material":"preprint","caption":"Graphic representation of the unbinned maximum likelihood fit performed to simulated events using the signal model underlined by equation~\\ref{eq:crystal-ball} and described in the text. Two representative signal mass hypotheses, namely $\\ma = 5 \\GeV$ (left) and $\\ma = 9 \\GeV$ (right), are shown for the ggF process. The data points represent the reconstructed di-muon invariant mass distribution as obtained from simulated signal events, whereas the solid colored line represents the analytical shape of the signal after a fit was performed. The lower panel shows the standard pull obtained when comparing the fitted p.d.f. to the simulated data. The error bars on the data points include only the uncertainty related to the simulation statistics.","label":"fig:Signal-Model","source":"arxiv","key":"a0090a01cf6b8dcb1b3694fd63e6c3e0","url":"https://inspirehep.net/files/a0090a01cf6b8dcb1b3694fd63e6c3e0"},{"filename":"Fitting_Background.png","material":"preprint","caption":"Illustration of the fit performed to data events selected in the CR using the background model underlined by equation~\\ref{eq:background-model} and described in the text. The data points represent the reconstructed di-muon invariant mass distribution in the observed data. The blue solid line represents the full background p.d.f. after having performed a fit to CR data. The dotted lines indicate each of the different components that are embedded in the combined background p.d.f.: first exponential function (light gray), second exponential function (brown), $J/\\psi(1S)$ (orange), $\\Psi(2S)$ (olive green), $\\Upsilon(S1)$ (dark gray), $\\Upsilon(S2)$ (cyan), and $\\Upsilon(S3)$ (red). The lower panel shows the difference between the combined background model conceived and the CR data. The green and yellow bands represent a visualization of the 68 and 95\\% CL uncertainties in the fit. A goodness of fit test based on the \\textit{saturated model} revealed a \\textit{p-value} of 0.12 for the fit depicted in this figure.","label":"fig:Background-Model","source":"arxiv","key":"1d8d633bef31bf3d28ebac55cf36d6a8","url":"https://inspirehep.net/files/1d8d633bef31bf3d28ebac55cf36d6a8"},{"filename":"Fitting_SR.png","material":"preprint","caption":"Invariant mass distribution of the muon pair in the SR after having performed a simultaneous background-only fit to the observed events in the SR and the CR. The black points represent the observed data, which has been binned for illustration purposes. The blue solid line represents the background prediction in the SR, while the dashed lines indicate the expected curves for four representative signal mass hypotheses: $\\ma = 5\\GeV$ (purple), $\\ma = 7\\GeV$ (cyan), $\\ma = 9\\GeV$ (yellow), and $\\ma = 11\\GeV$ (red). For this graphical representation, the signal has been normalized to the sum of the SM cross sections of the ggF and VBF processes, multiplied by a benchmark branching fraction of $\\mathcal{B}(\\text{h}_{125} \\rightarrow \\text{a}\\text{a} \\rightarrow \\mu^{-}\\mu^{+} c\\bar{c}) = 10^{-3}$. The lower panel shows the difference between the observed data in the SR and the expected SM background. The green and yellow bands represent a visualization of the 68 and 95\\% CL uncertainties in the fit. A goodness of fit test based on the \\textit{saturated model} revealed a \\textit{p-value} of 0.36 for the simultaneous fit illustrated above.","label":"fig:Final-Fit","source":"arxiv","key":"a6b8bd3b2a5674a63bd701f0c7847b3a","url":"https://inspirehep.net/files/a6b8bd3b2a5674a63bd701f0c7847b3a"},{"filename":"Limits_BR.png","material":"preprint","caption":"Observed and expected upper limits at 95\\%~CL on the product of the signal cross section and branching fraction $\\sigma/\\sigma_{\\text{SM}} \\cdot \\mathcal{B}(\\text{h}_{125} \\rightarrow \\text{a}\\text{a} \\rightarrow \\mu^{-}\\mu^{+} c\\bar{c})$ relative to the SM prediction. The solid and dashed lines correspond to the observed and median expected limits, respectively, while the green and yellow bands indicate the regions that contain 68\\% and 95\\% of the expected upper limits.","label":"fig:Limits-BR","source":"arxiv","key":"14373dd9355e2612f323495b8573386b","url":"https://inspirehep.net/files/14373dd9355e2612f323495b8573386b"},{"filename":"Exclusion_BRaa_Type_II_0.5.png","material":"preprint","caption":"Observed and expected upper limits at 95\\%~CL on $\\sigma/\\sigma_{\\text{SM}} \\cdot \\mathcal{B}(\\text{h}_{125} \\rightarrow \\text{a}\\text{a})$ as a function of \\ma for Type II (upper) and Type III (lower) 2HDM+S scenarios. The limits are computed assuming a value of $\\tan\\beta = 0.5$. The results of this search employing the CMS Open Data (16.4~\\invfb) are compared to several experimental results delivered by both the CMS and the ATLAS collaborations in the $\\mu\\mu\\mu\\mu$, $\\mu\\mu\\tau\\tau$, and $\\tau\\tau\\tau\\tau$ final states. The CMS and ATLAS results comprise at least 35.9~\\invfb of data.","label":"fig:2HDMS-exclusions","source":"arxiv","key":"f14f2f7cae2bf74257f71b516acb8aac","url":"https://inspirehep.net/files/f14f2f7cae2bf74257f71b516acb8aac"},{"filename":"Exclusion_BRaa_Type_III_0.5.png","material":"preprint","caption":"Observed and expected upper limits at 95\\%~CL on $\\sigma/\\sigma_{\\text{SM}} \\cdot \\mathcal{B}(\\text{h}_{125} \\rightarrow \\text{a}\\text{a})$ as a function of \\ma for Type II (upper) and Type III (lower) 2HDM+S scenarios. The limits are computed assuming a value of $\\tan\\beta = 0.5$. The results of this search employing the CMS Open Data (16.4~\\invfb) are compared to several experimental results delivered by both the CMS and the ATLAS collaborations in the $\\mu\\mu\\mu\\mu$, $\\mu\\mu\\tau\\tau$, and $\\tau\\tau\\tau\\tau$ final states. The CMS and ATLAS results comprise at least 35.9~\\invfb of data.","label":"fig:2HDMS-exclusions","source":"arxiv","key":"4d3fb45e7fa045f1225436a582652f70","url":"https://inspirehep.net/files/4d3fb45e7fa045f1225436a582652f70"}],"inspire_categories":[{"term":"Experiment-HEP","source":"arxiv"}],"first_author":{"emails":["danyer.perez.adan@rwth-aachen.de"],"affiliations_identifiers":[{"schema":"GRID","value":"grid.1957.a"},{"schema":"ROR","value":"https://ror.org/04xfq0f34"}],"full_name":"Perez Adan, Danyer","ids":[{"schema":"ORCID","value":"0000-0003-3416-0726"},{"schema":"INSPIRE BAI","value":"Danyer.Perez.Adan.1"}],"last_name":"Perez Adan","first_name":"Danyer","recid":1609724},"control_number":2912807,"dois":[{"source":"Springer","value":"10.1007/JHEP09(2025)096"},{"material":"publication","source":"arXiv","value":"10.1007/JHEP09(2025)096"}],"document_type":["article"],"texkeys":["PerezAdan:2025faj","Adan:2025gdx"],"abstracts":[{"source":"Springer","value":"A search is performed for pairs of light pseudoscalar bosons (a) produced from decays of the 125 GeV Higgs boson (h$_{125}$). The analysis is based on publicly available data collected in 2016 by the CMS experiment at the LHC in proton-proton collisions at a center-of-mass energy of 13 TeV. The amount of data analyzed corresponds to an integrated luminosity of 16.4 fb$^{−1}$. The analysis explores for the first time at the LHC the final state exhibiting two muons and two c-quarks, which originate from flavor-asymmetric decays of the pseudoscalar pair. The search probes the pseudoscalar boson mass interval comprised between 4 and 11 GeV, which represents a region where the light bosons exhibit a considerable Lorentz boost, and thus their decay products overlap. No significant deviation from the standard model expectation is observed. Model-independent upper limits at 95% confidence level are set on the product of the cross section and branching fraction for the $ {\\textrm{h}}_{125}\\to \\textrm{aa}\\to {\\mu}^{-}{\\mu}^{+}c\\overline{c} $ process relative to the standard model Higgs boson production cross section, reaching a minimum value close to 3.3 × 10$^{−4}$. The results are interpreted in the context of two Higgs doublets plus singlet models and compared to existing experimental results covering other decay channels. The exclusion limits obtained by this search improve the current constraints set by various LHC searches in scenarios where the coupling of the light boson to up-type quarks is enhanced.","abstract_source_suggest":{"input":"Springer"}},{"source":"arXiv","value":"A search is performed for pairs of light pseudoscalar bosons (a) produced from decays of the 125 GeV Higgs boson ($\\text{h}_{125}$). The analysis is based on publicly available data collected in 2016 by the CMS experiment at the LHC in proton-proton collisions at a center-of-mass energy of 13 TeV. The amount of data analyzed corresponds to an integrated luminosity of 16.4 $\\text{fb}^{-1}$. The analysis explores for the first time at the LHC the final state exhibiting two muons and two c-quarks, which originate from flavor-asymmetric decays of the pseudoscalar pair. The search probes the pseudoscalar boson mass interval comprised between 4 and 11 GeV, which represents a region where the light bosons exhibit a considerable Lorentz boost, and thus their decay products overlap. No significant deviation from the standard model expectation is observed. Model-independent upper limits at 95% confidence level are set on the product of the cross section and branching fraction for the ${\\text{h}_{125} \\rightarrow \\text{a}\\text{a} \\rightarrow μ^{-}μ^{+} c\\bar{c}}$ process relative to the standard model Higgs boson production cross section, reaching a minimum value close to $3.3 \\times 10^{-4}$. The results are interpreted in the context of two Higgs doublets plus singlet models and compared to existing experimental results covering other decay channels. The exclusion limits obtained by this search improve the current constraints set by various LHC searches in scenarios where the coupling of the light boson to up-type quarks is enhanced.","abstract_source_suggest":{"input":"arXiv"}}],"primary_arxiv_category":["hep-ex"],"titles":[{"source":"Springer","title":"Search for an exotic decay of the 125 GeV Higgs boson to a pair of light pseudoscalars in the final state of two muons and two c-quarks in proton-proton collisions at $ \\sqrt{s}=13 $ TeV with CMS Open Data"},{"source":"arXiv","title":"Search for an exotic decay of the 125 GeV Higgs boson to a pair of light pseudoscalars in the final state of two muons and two c-quarks in proton-proton collisions at $\\sqrt{s} = 13\\;\\text{TeV}$ with CMS Open Data"}],"imprints":[{"date":"2025-09-11"}],"curated":true}},{"created":"2019-06-17T00:00:00+00:00","updated":"2024-06-21T19:53:11.733872+00:00","id":"1740021","links":{"bibtex":"https://inspirehep.net/api/literature/1740021?format=bibtex","latex-eu":"https://inspirehep.net/api/literature/1740021?format=latex-eu","latex-us":"https://inspirehep.net/api/literature/1740021?format=latex-us","json":"https://inspirehep.net/api/literature/1740021?format=json","json-expanded":"https://inspirehep.net/api/literature/1740021?format=json-expanded","cv":"https://inspirehep.net/api/literature/1740021?format=cv","citations":"https://inspirehep.net/api/literature/?q=refersto%3Arecid%3A1740021"},"metadata":{"citation_count":3,"citation_count_without_self_citations":3,"authors":[{"raw_affiliations":[{"value":"California Institute of Technology"}],"full_name_unicode_normalized":"pata, joosep","full_name":"Pata, Joosep","record":{"$ref":"https://inspirehep.net/api/authors/1077949"},"ids":[{"schema":"INSPIRE BAI","value":"J.Pata.1"}],"last_name":"Pata","affiliations":[{"record":{"$ref":"https://inspirehep.net/api/institutions/902711"},"value":"Caltech"}],"signature_block":"PATj","first_name":"Joosep","uuid":"db0ad33d-8687-4e63-abc9-8ff9ac67a469","recid":1077949},{"raw_affiliations":[{"value":"California Institute of Technology"}],"full_name_unicode_normalized":"spiropulu, maria","full_name":"Spiropulu, Maria","curated_relation":true,"record":{"$ref":"https://inspirehep.net/api/authors/987865"},"ids":[{"schema":"INSPIRE BAI","value":"M.Spiropulu.1"}],"last_name":"Spiropulu","affiliations":[{"record":{"$ref":"https://inspirehep.net/api/institutions/902711"},"value":"Caltech"}],"signature_block":"SPARAPALm","first_name":"Maria","uuid":"e6bf71cc-fded-46d4-b554-64f07e7e76a9","recid":987865}],"citeable":true,"$schema":"https://inspirehep.net/schemas/records/hep.json","keywords":[{"schema":"INSPIRE","value":"programming"},{"schema":"INSPIRE","value":"CMS"},{"schema":"INSPIRE","value":"data management"},{"schema":"INSPIRE","value":"multiprocessor: graphics"},{"schema":"INSPIRE","value":"statistical analysis"},{"schema":"INSPIRE","value":"performance"}],"references":[{"reference":{"dois":["10.1016/j.cpc.2009.08.005"],"arxiv_eprint":"1508.07749","label":"1","publication_info":{"journal_volume":"180","artid":"2499","year":2009,"page_start":"2499","journal_title":"Comput.Phys.Commun."},"authors":[{"full_name":"Antcheva, I."}]},"raw_refs":[{"schema":"text","value":"[1] I. Antcheva et al., Comput. Phys. Commun. 180, 2499 (2009) doi:10.1016/j.cpc.2009.08.005 [arXiv:1508.07749 [physics.data-an]]."}],"curated_relation":false,"record":{"$ref":"https://inspirehep.net/api/literature/839881"}},{"reference":{"dois":["10.5281/zenodo.3491423"],"label":"2","authors":[{"full_name":"Pata, J."}]},"raw_refs":[{"schema":"text","value":"[2] J. Pata et al. DOI: 10.5281/zenodo.3491423"}]},{"reference":{"urls":[{"value":"https://github.com/scikit-hep/uproot"}],"label":"3","authors":[{"full_name":"Pivarski, J."}]},"raw_refs":[{"schema":"text","value":"[3] J. Pivarski et al. https://github.com/scikit-hep/uproot"}]},{"reference":{"urls":[{"value":"https://github.com/scikit-hep/awkward-array"}],"label":"4","authors":[{"full_name":"Pivarski, J."}]},"raw_refs":[{"schema":"text","value":"[4] J. Pivarski et al. https://github.com/scikit-hep/awkward-array"}]},{"reference":{"label":"5","publication_info":{"year":2015},"misc":["November. Numba: A llvmbased python jit compiler. In Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC (p. 7). ACM"],"authors":[{"full_name":"Lam, S.K."},{"full_name":"Pitrou, A."},{"full_name":"Seibert, S."}]},"raw_refs":[{"schema":"text","value":"[5] Lam, S.K., Pitrou, A. and Seibert, S., 2015, November. Numba: A llvmbased python jit compiler. In Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC (p. 7). ACM."}]},{"reference":{"imprint":{"publisher":"IOP"},"label":"6","publication_info":{"year":2018},"misc":["September. Optimizing ROOT IO For Analysis. In Journal of Physics: Conference Series (Vol. 1085, No. 3, p. 032012) / Publishing"],"authors":[{"full_name":"Bockelman, B."},{"full_name":"Zhang, Z."},{"full_name":"Pivarski, J."}]},"raw_refs":[{"schema":"text","value":"[6] Bockelman, B., Zhang, Z. and Pivarski, J., 2018, September. Optimizing ROOT IO For Analysis. In Journal of Physics: Conference Series (Vol. 1085, No. 3, p. 032012). 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DOIs: 1) 10.7483/OPENDATA.CMS.42GY.2VJI 2) 10.7483/OPENDATA.CMS.2DSE.HYDF 3) 10.7483/OPENDATA.CMS.7RZ3.0BXP 4) 10.7483/OPENDATA.CMS.ARKO.6NV3 5) 10.7483/OPENDATA.CMS.REHM.JKUH 6) 10.7483/OPENDATA.CMS.DELK.2V7R, 7) 10.7483/OPENDATA.CMS.HHCJ.TVXH, 8) 10.7483/OPENDATA.CMS.DELK.2V7R, 9) 10.7483/OPENDATA.CMS.KAYE.XLAH,"}]},{"reference":{"collaborations":["CMS collaboration"],"dois":["10.7483/OPENDATA.CMS.9A4E.7SIR"],"label":"9","publication_info":{"year":2017},"misc":["SingleMu primary dataset in AOD format from Run of 2012 CERN Open Data Portal. (/SingleMu/Run2012[B-C]22Jan2013-v1/AOD). DOIs: 1)"]},"raw_refs":[{"schema":"text","value":"[9] CMS collaboration (2017). SingleMu primary dataset in AOD format from Run of 2012 CERN Open Data Portal. (/SingleMu/Run2012[B-C]22Jan2013-v1/AOD). DOIs: 1) 10.7483/OPENDATA.CMS.9A4E.7SIR 2) 10.7483/OPENDATA.CMS.IYVQ.1J0W"}]},{"reference":{"dois":["10.7483/OPENDATA.CMS.IYVQ.1J0W"],"label":"9","misc":["2)"]},"raw_refs":[{"schema":"text","value":"[9] CMS collaboration (2017). SingleMu primary dataset in AOD format from Run of 2012 CERN Open Data Portal. (/SingleMu/Run2012[B-C]22Jan2013-v1/AOD). DOIs: 1) 10.7483/OPENDATA.CMS.9A4E.7SIR 2) 10.7483/OPENDATA.CMS.IYVQ.1J0W"}]},{"reference":{"urls":[{"value":"https://github.com/cms-opendata-analyses/"}],"label":"10","misc":["AOD2NanoAODOutreachTool"]},"raw_refs":[{"schema":"text","value":"[10] https://github.com/cms-opendata-analyses/ AOD2NanoAODOutreachTool"}]}],"number_of_pages":8,"legacy_creation_date":"2019-06-17","preprint_date":"2019-06-14","author_count":2,"earliest_date":"2019-06-14","facet_author_name":["987865_Maria Spiropulu","1077949_Joosep Pata"],"core":true,"license":[{"license":"arXiv nonexclusive-distrib 1.0","material":"preprint","url":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/"}],"_oai":{"sets":["Literature"],"id":"oai:inspirehep.net:1740021","updated":"2024-06-21T19:53:11.733872"},"arxiv_eprints":[{"categories":["physics.data-an","cs.DC","physics.comp-ph"],"value":"1906.06242"}],"referenced_authors_bais":["E.A.J.M.Offermann.1","F.Rademakers.1","P.Russo.1","A.Kreshuk.1","G.Ganis.1","B.Bellenot.1","N.Buncic.1","O.Couet.1","M.Biskup.1","M.Tadel.1","V.Fine.1","I.Antcheva.1","S.Panacek.1","A.Gheata.2","N.A.Naumann.1","Rene.Brun.1","D.Marcos.Segura.1","L.M.Franco.2","V.Onuchin.1","P.Canal.1","R.Maunder.1","D.Gonzalez.Maline.1","J.Iwaszkiewicz.1","L.Moneta.1","M.Ballintijn.2","D.Casadei.1","M.Goto.2"],"figures":[{"filename":"jagged_matrices.png","caption":"A visual representation of the jagged data structure of the jet $p_T$, $\\eta$ and $\\phi$ content in 50 simulated events.  On the leftmost figure, we show the number of jets per event, one event per row, derived from the offset array. In the three rightmost columns, we show the jet content in events, visualizing the $p_T$, $\\eta$ and $\\phi$ of the first 20 jets for each event.","source":"arxiv","key":"6afe16d958a10bd48041f70738fc31b7","url":"https://inspirehep.net/files/6afe16d958a10bd48041f70738fc31b7"},{"filename":"jagged.png","caption":"A visual representation of the jagged data structure of the jet $p_T$, $\\eta$ and $\\phi$ content in 50 simulated events.  On the diagram on the left, we illustrate how three events with a varying number of jets are recorded as contiguous arrays. On the figure on the right, we show the number of jets per event, one event per row, which is derived from the offset array. In the three rightmost columns, we show the jet content in events, visualizing the $p_T$, $\\eta$ and $\\phi$ of the first 10 jets for each event.","source":"arxiv","key":"187771c81995c5c1fcad076046398638","url":"https://inspirehep.net/files/187771c81995c5c1fcad076046398638"},{"filename":"analysis_scaling.png","caption":"Extrapolated processing time for the $H(\\mu\\mu)$ analysis on 1 billion events and scaling with respect to analysis complexity. More complex analyses are emulated by iterating same physics code on data already loaded to memory either 5x or 10x. We compare the expected time of a naive reanalysis on the CPU backend (blue) with respect to reanalyzing a loaded batch on the CPU backend (orange) and on the GPU backend (green). A naive reanalysis scales linearly with complexity, whereas analyzing data already in memory ensures the code is compute-bound rather than IO-bound. The GPU backend provides the best scaling with respect to analysis complexity.","source":"arxiv","key":"69f442a008bd0ef8ec6cfe02836ba10e","url":"https://inspirehep.net/files/69f442a008bd0ef8ec6cfe02836ba10e"}],"legacy_version":"20191023070206.0","inspire_categories":[{"term":"Data Analysis and Statistics","source":"arxiv"},{"term":"Computing","source":"arxiv"},{"term":"Data Analysis and Statistics"},{"term":"Computing"}],"first_author":{"affiliations_identifiers":[{"schema":"ROR","value":"https://ror.org/05dxps055"}],"full_name":"Pata, Joosep","last_name":"Pata","first_name":"Joosep","recid":1077949},"control_number":1740021,"document_type":["article"],"texkeys":["Pata:2019snm"],"abstracts":[{"source":"arXiv","value":"At high energy physics experiments, processing billions of records of structured numerical data from collider events to a few statistical summaries is a common task. The data processing is typically more complex than standard query languages allow, such that custom numerical codes are used. At present, these codes mostly operate on individual event records and are parallelized in multi-step data reduction workflows using batch jobs across CPU farms. Based on a simplified top quark pair analysis with CMS Open Data, we demonstrate that it is possible to carry out significant parts of a collider analysis at a rate of around a million events per second on a single multicore server with optional GPU acceleration. This is achieved by representing HEP event data as memory-mappable sparse arrays of columns, and by expressing common analysis operations as kernels that can be used to process the event data in parallel. We find that only a small number of relatively simple functional kernels are needed for a generic HEP analysis. The approach based on columnar processing of data could speed up and simplify the cycle for delivering physics results at HEP experiments. We release the \\texttt{hepaccelerate} prototype library as a demonstrator of such methods.","abstract_source_suggest":{"input":"arXiv"}}],"primary_arxiv_category":["physics.data-an"],"titles":[{"source":"arXiv","title":"Processing Columnar Collider Data with GPU-Accelerated Kernels"},{"source":"arXiv","title":"Processing Columnar Collider Data withGPU-Accelerated Kernels"},{"source":"arXiv","title":"hepaccelerate: Fast Analysis of Columnar Collider Data"}],"curated":true}},{"created":"2021-04-23T00:00:00+00:00","updated":"2025-01-23T19:13:15.414149+00:00","id":"1860231","links":{"bibtex":"https://inspirehep.net/api/literature/1860231?format=bibtex","latex-eu":"https://inspirehep.net/api/literature/1860231?format=latex-eu","latex-us":"https://inspirehep.net/api/literature/1860231?format=latex-us","json":"https://inspirehep.net/api/literature/1860231?format=json","json-expanded":"https://inspirehep.net/api/literature/1860231?format=json-expanded","cv":"https://inspirehep.net/api/literature/1860231?format=cv","citations":"https://inspirehep.net/api/literature/?q=refersto%3Arecid%3A1860231"},"metadata":{"documents":[{"filename":"PoS(ICHEP2020)908.pdf","attachment":{"content":"P\no\nS\n(\nI\nC\nH\nE\nP\n2\n0\n2\n0\n)\n9\n0\n8\n\nData Analysis with GPU-Accelerated Kernels\n\nJ. Pata,0 I. Dutta,0,∗ N. Lu,0 J. R. Vlimant,0 H. Newman,0 M. Spiropulu,0 C. Reissel1\n\nand D. Ruini1\n0California Institute of Technology,\n1200 E California Blvd, Pasadena, CA, USA 91125\n\n1ETH Zurich\nRämistrasse 101, 8092 Zürich, Switzerland\n\nE-mail: idutta@caltech.edu, jpata@caltech.edu, reielc@ethz.ch\n\nAtHEP experiments, processing billions of records of structured numerical data can be a bottleneck\nin the analysis pipeline. This step is typically more complex than current query languages allow,\nsuch that numerical codes are used. As highly parallel computing architectures are increasingly\nimportant in the computing ecosystem, it may be useful to consider how accelerators such as GPUs\ncan be used for data analysis. Using CMS and ATLAS Open Data, we implement a benchmark\nphysics analysis with GPU acceleration directly in Python based on efficient computational kernels\nusing Numba/LLVM, resulting in an order of magnitude throughput increase over a pure CPU-\nbased approach. We discuss the implementation and performance benchmarks of the physics\nkernels on CPU and GPU targets. We demonstrate how these kernels are combined to a modern\nML-intensive workflow to enable efficient data analysis on high-performance servers and remark\non possible operational considerations.\n\n40th International Conference on High Energy physics - ICHEP 2020\nJuly 28 - August 6, 2020\nPrague, Czech Republic (virtual meeting)\n\n∗Speaker\n\n© Copyright owned by the author(s) under the terms of the Creative Commons\nAttribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). https://pos.sissa.it/\n\nmailto:idutta@caltech.edu\nmailto:jpata@caltech.edu\nmailto:reielc@ethz.ch\nhttps://pos.sissa.it/\n\n\nP\no\nS\n(\nI\nC\nH\nE\nP\n2\n0\n2\n0\n)\n9\n0\n8\n\nData Analysis with GPU-Accelerated Kernels I. Dutta\n\n1. Introduction\n\nAt the general-purpose experiments of the Large Hadron Collider such as CMS or ATLAS, the\ndata formats typically consist of columns of physics related variables or features for the recorded\nparticles such as electrons, muons, jets and photons for each event in billions of rows. In addition to\nthe columns of purely kinematic information, each particle carries other reconstruction details, for\nexample, the number of tracker layers with an associated hit for a muon. A typical event for such\nreduced data formats are on the order of a few kilobytes per event.\n\nA typical physics analysis at the LHC requires tens to hundreds of iterations over such datasets\nusing batch jobs. By reducing the complexity and increasing the speed of these workflows, HEP\nexperiments could deliver results from large datasets with faster turn-around times.\n\nRecently, heterogeneous and highly parallel computing architectures beyond consumer x86\nprocessors such as GPUs, TPUs and FPGAs have become increasingly prevalent in scientific\ncomputing. We investigate the use of array-based computational kernels that are well-suited\nfor parallel architectures for carrying out HEP data analysis using the example kernels in the\nhepaccelerate [1] library.\n\n2. Data structure\n\nWe can represent HEP collider data as 2D matrices, where the rows correspond to events and\ncolumns correspond to features in the event. The standard HEP software framework based on ROOT\nrepresents this as dynamically-sized arrays and complete C++ classes with arbitrary structure [2].\n\nBased on the approach first introduced in the uproot [3] and awkward-array [4] python\nlibraries, HEP data files with a varying number of particles per event can be represented and\nefficiently loaded as sparse arrays with an underlying one-dimensional array for a single feature.\nEvent boundaries are encoded in an offset array that records the particle count per event. Therefore,\nthe full structure of # events, \" particles in total, can be represented by a contiguous offset array\nof size # + 1 and a contiguous data array of size \" for each particle feature.\n\n3. Computational kernels\n\nIn the context of this report, a kernel is a function that is evaluated on the elements of an array\nto transform the underlying data, for example, computing the square root of all values in an array.\nWhen the individual kernel calls across the data are independent of each other, they can be evaluated\nin parallel over the data using single-instruction, multiple-data (SIMD) processors.\n\nWe note that the columnar data analysis approach based on single-threaded kernels is already\nrecognized in HEP [5]. Our contribution is to further extend the computational efficiency of the\nkernels to parallel hardware such as multi-threaded CPUs and propose a GPU implementation.\n\nA prototypical HEP-specific kernel would be to find the scalar sum �) =\n∑\n\n8∈event ?\n8\n)\nof all\n\nparticles passing some quality criteria in an event. We show the Python implementation for this on\nListing 1 (for multi-threading on CPUs) and on Listing 2 (for GPUs).\n\nOther generic kernels that turn out to be useful are related to finding the minimum or maximum\nvalue within the event offsets (for example, finding the jet with the highest ?) in an event) or\n\n2\n\n\n\nP\no\nS\n(\nI\nC\nH\nE\nP\n2\n0\n2\n0\n)\n9\n0\n8\n\nData Analysis with GPU-Accelerated Kernels I. Dutta\n\n1 def sum_ht(\n2 pt_data , offsets ,\n\n3 mask_rows , mask_content ,\n\n4 out):\n\n5\n6 N = len(offsets) - 1\n7 M = len(pt_data)\n8\n9 #loop over events in parallel\n\n10 for iev in prange(N):\n11 if not mask_rows[iev]:\n12 continue\n13\n14 #indices of the particles in this event\n\n15 i0 = offsets[iev]\n\n16 i1 = offsets[iev + 1]\n\n17\n18 #loop over particles in this event\n\n19 for ielem in range(i0 , i1):\n20 if mask_content[ielem]:\n21 out[iev] += pt_data[ielem]\n\nListing 1: Python code for the kernel computing the scalar sum of selected particle momenta �) . The\ninputs are pt_data, an \"-element array of ?) data for all the particles, the # + 1-element offsets\narray with the indices between the events in the particle collections, as well masks for events and\nparticles that should be considered. On line 10, the kernel is executed in parallel over the events using\nthe Numba prange iterator, which creates multi-threaded code across the loop iterations. On line 19,\nthe particles in the event are iterated over sequentially.\n\nretrieving or setting the <-th value of an array within the event offsets. Several such kernels can\nalready be found in [1].\n\nThese kernels have been implemented in Python and just-in-time compiled to either multi-\nthreaded CPU code using the Numba package [6] or using CUDA [7] for GPUs. We have chosen\nPython and Numba to implement the kernels in the spirit of quickly prototyping this idea, but this\napproach is not restricted to a particular programming environment.\n\n4. Analysis benchmark\n\nCMS Open Data\n\nWe benchmark this approach in a prototypical top quark pair analysis using CMS Open Data\nfrom 2012 [8]. The datasets are processed from the experiment-specific format to a columnar\nformat using a publicly-available tool [9] and correspond to about 60GB of simulation and 40GB\nof data with the single muon trigger . Our objective here is to replicate the computing conditions\nthat are encountered in HEP data analyses and not to reproduce published physics results.\n\nThe benchmark analysis implements the following features in a single end-to-end pass:\n\n• event selection based on event variables: trigger bit, missing transverse energy selection\n\n3\n\n\n\nP\no\nS\n(\nI\nC\nH\nE\nP\n2\n0\n2\n0\n)\n9\n0\n8\n\nData Analysis with GPU-Accelerated Kernels I. Dutta\n\n1 @cuda.jit\n\n2 def sum_ht_cudakernel(\n3 pt_data , offsets ,\n\n4 mask_rows , mask_content ,\n\n5 out):\n\n6\n7 xi = cuda.grid (1)\n\n8 xstride = cuda.gridsize (1)\n\n9 for iev in range(xi, offsets.shape [0]-1, xstride ):\n10 if mask_rows[iev]:\n11 start = np.uint64(offsets[iev])\n\n12 end = np.uint64(offsets[iev + 1])\n\n13\n14 #loop over particles in this event\n\n15 for ielem in range(start , end):\n16 if mask_content[ielem]:\n17 out[iev] += pt_data[ielem]\n\nListing 2: Python code for the kernel computing the scalar sum of selected particle momenta �) .\nThe inputs are pt_data, an array of ?) data for all the particles, the offsets array with the indices\nbetween the events in the particle collections, as well masks for events and particles that should be\nconsidered. On line 9, the kernel is executed in a GPU compatible style which is parallel over the events\nusing CUDA. On line 15, the particles in the event are iterated over sequentially.\n\n• object selections : jet, lepton selection based on ?) , [\n\n• matching of pairs of objects: jet-lepton Δ' matching, jet to generator jet matching\n\n• event weight computation based on histogram look-ups: pileup, lepton efficiency corrections\n\n• jet energy corrections based on histogram look-ups: in-situ jet energy systematics, increasing\nthe computational complexity of the analysis by about 40x\n\n• high-level variable reconstruction: top quark candidate from jet triplet with invariant mass\nclosest to \" = 172 GeV\n\n• evaluation of ' 40 typical high-level inputs to a deep neural network (DNN)\n\n• Multi-layer, feed-forward DNN evaluation using tensorflow\n\n• saving all DNN inputs and outputs, along with systematic variations, to histograms (' 1000\nindividual histograms)\n\nWe perform two benchmark scenarios of this analysis: one with a partial set of systematic\nuncertainties to emulate a simpler IO-limited analysis, and one with the full set of systematic\nuncertainties to test a compute-bound workload. The timing results from the benchmark are\nreported on Fig. 1a. In the former case, we observe the GPU-accelerated version performing about\n12x faster than a single multi-threaded CPU. For the latter case, where the main workload is repeated\naround 40x, a single GPU is about 15x faster than 1 multi-threaded CPU.\n\n4\n\n\n\nP\no\nS\n(\nI\nC\nH\nE\nP\n2\n0\n2\n0\n)\n9\n0\n8\n\nData Analysis with GPU-Accelerated Kernels I. Dutta\n\n0 50 100 150 200\nRuntime (minutes)\n\n1x CPU\n(24 threads)\n\n8x GPU\n(16 streams)\n\nAnalysis runtime on a multi-GPU system:\n2.71E+08 events,\n\nGPU speedup 11.7x\n\n(a) (b)\n\nFigure 1: (a) Benchmarks of the full analysis with 270M events, 100GB of numerical data on a multi-GPU\nsystem. We find that by using 8x nVidia GTX 1080 GPUs, 2 compute streams per device, we can reduce the\nanalysis runtime by a factor of 12x, compared to using multiple threads on the CPU alone. (b) The 4 lepton\ninvariant mass from the benchmark analysis of � → // → 4; implemented using ATLAS Open Data.\n\nWe have also found that it is beneficial to ensure that the kernels are called on sufficiently\nlarge datasets to reduce the overhead of kernel scheduling with respect to the time spent in the\ncomputation.\n\nIt is promising to see that most of the physics analysis methods can be implementedwith relative\nease on a GPU, such that with further optimizations, a small number of multi-GPU machines might\nbe a viable option in the future (depending upon the availability and pricing of resources).\n\nATLAS Open Data\n\nTo demonstrate the capability of [1] to run on data formats from different collider experiments,\nwe tried to reproduce the classic � → // → 4; analysis with the 13 TeV ATLAS Open Data [10].\nThe benchmark analysis implements the following features:\n\n• object selection based on lepton variables: ?) , [, charge\n\n• event weight computation based on lepton efficiency corrections, trigger weights\n\n• high-level variable reconstruction: 4 lepton invariant mass closest to \" = 125 GeV\n\nThe invariant 4 lepton mass is shown in Fig. 1b.\n\n5. Summary\n\nWe demonstrate that it is possible to carry out prototypical end-to-end high-energy physics\ndata analysis using a relatively simple code in a HPC context. This is achieved with efficient input\ndata preparation, array processing approaches and a small number of specialized kernels for jagged\narrays implemented in Python using the Numba package. It is also possible to offload parts of these\narray computations to accelerators such as GPUs, which are highly efficient at parallel processing.\n\n5\n\n\n\nP\no\nS\n(\nI\nC\nH\nE\nP\n2\n0\n2\n0\n)\n9\n0\n8\n\nData Analysis with GPU-Accelerated Kernels I. Dutta\n\nWe demonstrate that such kernels can run an order of magnitude faster on a multi-GPU machine as\ncompared to using a multi-threaded CPU approach.\n\nAcknowledgments\n\nThe availability of the excellent open-source Python libraries uproot, awkward, coffea,\nnumba, cupy and numpy as well as the CERN Open Data was imperative for this project. We\nare grateful to the developers, maintainers and funders of these open-source projects. This work\nwas partly conducted at \"iBanks\", the AI GPU cluster at Caltech. We acknowledge NVIDIA,\nSuperMicro and the Kavli Foundation for their support of \"iBanks\". This work is supported by the\nthe Prime NSF Tier2 award 1624356 and the Caltech HEP DOE award DE-SC0011925.\n\nReferences\n\n[1] J. Pata et al. DOI: 10.5281/zenodo.3491423\n\n[2] I. Antcheva et al., Comput. Phys. Commun. 180, 2499 (2009) doi:10.1016/j.cpc.2009.08.005\n[arXiv:1508.07749 [physics.data-an]].\n\n[3] J. Pivarski et al. https://github.com/scikit-hep/uproot\n\n[4] J. Pivarski et al. https://github.com/scikit-hep/awkward-array\n\n[5] The Coffea Team, coffea v0.6.8, 10.5281/zenodo.3358981 (2019)\n\n[6] Lam, S.K., Pitrou, A. and Seibert, S., 2015, November. Numba: A llvm-based python jit\ncompiler. In Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in\nHPC (p. 7). ACM.\n\n[7] Nvidia CUDA Home page https://developer.nvidia.com/cuda-zone\n\n[8] CMS collaboration (2017). Simulated datasets in AODSIM format for 2012 collision data.\nCERN Open Data Portal. DOIs:\n\n1. 10.7483/OPENDATA.CMS.42GY.2VJI\n2. 10.7483/OPENDATA.CMS.2DSE.HYDF\n3. 10.7483/OPENDATA.CMS.7RZ3.0BXP\n4. 10.7483/OPENDATA.CMS.ARKO.6NV3\n5. 10.7483/OPENDATA.CMS.REHM.JKUH\n6. 10.7483/OPENDATA.CMS.DELK.2V7R\n7. 10.7483/OPENDATA.CMS.HHCJ.TVXH\n8. 10.7483/OPENDATA.CMS.DELK.2V7R\n9. 10.7483/OPENDATA.CMS.KAYE.XLAH\n\n[9] https://github.com/cms-opendata-analyses/AOD2NanoAODOutreachTool\n\n[10] ATLAS collaboration (2020)- 13 TeV samples collection with at least 4 leptons (electrons or\nmuons) http://opendata.cern.ch/record/15005\n\n6\n\nhttps://github.com/scikit-hep/uproot\nhttps://github.com/scikit-hep/awkward-array\nhttps://developer.nvidia.com/cuda-zone\nhttps://github.com/cms-opendata-analyses/AOD2NanoAODOutreachTool\nhttp://opendata.cern.ch/record/15005\n\n\tIntroduction\n\tData structure\n\tComputational kernels\n\tAnalysis benchmark\n\tSummary"},"fulltext":true,"source":"PoS","key":"18faa7b1e68dc70d720dcf3a7eb5bd21","url":"https://inspirehep.net/files/18faa7b1e68dc70d720dcf3a7eb5bd21"}],"citation_count":2,"citation_count_without_self_citations":1,"publication_info":[{"journal_volume":"ICHEP2020","artid":"908","conference_record":{"$ref":"https://inspirehep.net/api/conferences/1677252"},"year":2021,"journal_record":{"$ref":"https://inspirehep.net/api/journals/1213080"},"page_start":"908","journal_title":"PoS","parent_record":{"$ref":"https://inspirehep.net/api/literature/1859861"},"cnum":"C20-07-30"}],"authors":[{"full_name_unicode_normalized":"pata, joosep","full_name":"Pata, Joosep","record":{"$ref":"https://inspirehep.net/api/authors/1077949"},"ids":[{"schema":"INSPIRE 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Analysis and Statistics"}],"urls":[{"description":"eConf","value":"https://www.slac.stanford.edu/econf/C210711/"}],"first_author":{"affiliations_identifiers":[{"schema":"ROR","value":"https://ror.org/02jbv0t02"}],"full_name":"Bailey, Stephen","last_name":"Bailey","first_name":"Stephen","recid":1017638},"control_number":2054747,"document_type":["article","conference paper"],"texkeys":["Bailey:2022tdz"],"abstracts":[{"source":"arXiv","value":"We make the case for the systematic, reliable preservation of event-wise data, derived data products, and executable analysis code. This preservation enables the analyses' long-term future reuse, in order to maximise the scientific impact of publicly funded particle-physics experiments. We cover the needs of both the experimental and theoretical particle physics communities, and outline the goals and benefits that are uniquely enabled by analysis recasting and reinterpretation. We also discuss technical challenges and infrastructure needs, as well as sociological challenges and changes, and give summary recommendations to the particle-physics community.","abstract_source_suggest":{"input":"arXiv"}}],"primary_arxiv_category":["hep-ph"],"titles":[{"source":"arXiv","title":"Data and Analysis Preservation, Recasting, and Reinterpretation"}],"curated":true}},{"created":"2017-04-26T00:00:00+00:00","updated":"2023-03-06T13:17:02.471445+00:00","id":"1596537","links":{"bibtex":"https://inspirehep.net/api/literature/1596537?format=bibtex","latex-eu":"https://inspirehep.net/api/literature/1596537?format=latex-eu","latex-us":"https://inspirehep.net/api/literature/1596537?format=latex-us","json":"https://inspirehep.net/api/literature/1596537?format=json","json-expanded":"https://inspirehep.net/api/literature/1596537?format=json-expanded","cv":"https://inspirehep.net/api/literature/1596537?format=cv","citations":"https://inspirehep.net/api/literature/?q=refersto%3Arecid%3A1596537"},"metadata":{"documents":[{"filename":"CO2016_329.pdf","description":"Link to ATLAS/CMS Note","key":"a0fdac781ae32c69955601cf2e4630c5","url":"https://inspirehep.net/files/a0fdac781ae32c69955601cf2e4630c5"},{"filename":"PoS(ICHEP2016)335.pdf","attachment":{"content":"P\no\nS\n(\nI\nC\nH\nE\nP\n2\n0\n1\n6\n)\n3\n3\n5\n\nHEP data for everyone: CERN open data and the\nATLAS and CMS experiments\n\nThomas McCauley for the ATLAS and CMS Collaborations∗\n\nUniversity of Notre Dame (US)\nE-mail: thomas.mccauley@cern.ch\n\nA cornerstone of good scientific practice is to make results available to the public. This is espe-\ncially true for experiments at the LHC at CERN where public investment in fundamental research\nis significant and long-standing. As part of their commitment to open access and public engage-\nment the ATLAS and CMS collaborations have made several large datasets available to the public.\nThere are many challenges posed in presenting complex and high-level data to the public in an\naccessible and meaningful way. We describe the solutions to these challenges, part of which is\nthe creation and use of the CERN Open Data Portal and the content found therein. Furthermore\nwe describe the impact and future plans of the ATLAS and CMS open access efforts including\nfuture releases of data and accompanying educational material.\n\n38th International Conference on High Energy Physics\n3-10 August 2016\nChicago, USA\n\n∗Speaker.\n\nc© Copyright owned by the author(s) under the terms of the Creative Commons\nAttribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). http://pos.sissa.it/\n\nmailto:thomas.mccauley@cern.ch\n\n\nP\no\nS\n(\nI\nC\nH\nE\nP\n2\n0\n1\n6\n)\n3\n3\n5\n\nHEP data for everyone: CERN open data and the ATLAS and CMS experiments\nThomas McCauley for the ATLAS and CMS Collaborations\n\n1. The ATLAS and CMS experiments\n\nATLAS [1] and CMS [2] are the two general-purpose experiments at the Large Hadron Col-\nlider (LHC) at CERN. Both share a broad physics program including studies of Standard Model\nphysics, searches for SUSY and other physics beyond the Standard Model, and measurement of the\nproperties of the Higgs Boson, the particle discovered by ATLAS and CMS in 2012. The discovery\nof the Higgs was the culmination of Run 1 at the LHC and as of this writing the LHC has fin-\nished delivering proton-proton collisions for Run 2 in 2016, having delivered 41 fb−1 of integrated\nluminosity at\n\n√\ns = 13 TeV to each experiment.\n\n2. Open Data\n\nThe data stored from the LHC collisions is on the order of hundreds of petabytes and the LHC\nexperiments have committed to make some of this data available to the public. The motivation for\ndoing so was expressed by the then-Director General of CERN, Rolf Heuer when the first public\nLHC data was released in 2014: \"We hope these open data will support and inspire the global\nresearch community, including students and citizen scientists\".\n\nReflecting their commitment to open-access ATLAS and CMS have both drafted and adopted\nopen-access policies [3, 4]. Both policies use common notions of levels of access to the data where\neach increasing level reflects the increasing complexity and information provided and the difficulty\nin delivering the data in a meaningful and useful way. Level 1 describes data directly related to\npublications. For example, the numerical data used in the production of a plot. Level 2 describes\nsimplified data suitable for education and outreach. Level 3 describes \"analysis-level\" reconstructed\ndata, simulation, and software, i.e. the data and tools needed and used by the physicists for analysis.\nFinally, Level 4 describes the raw data. These policies also include details on embargo periods,\nlicensing, and data reuse.\n\nThe first and most enduring use of public data from the LHC is education. One example are\nthe masterclasses [5]: simplified analyses of LHC data aimed at the high-school level. The success\nof such programs as the masterclasses was one of the favorable factors considered by the LHC\nexperiments when further data releases and open-access policies were discussed.\n\n2.1 Challenges\n\nIn order to make it useful and useable for the public several challenges have to be overcome.\nFirstly, the datasets involved are large and complex. Datasets of terabytes and petabytes in size\npresent problems of storage, distribution and processing. Furthermore the datasets reflect the com-\nplexity of the experiments from which they are produced. In order to read, process, and anal-\nyse the data large custom-made software frameworks are required, the use of which requires not-\ninconsiderable software experience. Finally there is the physics knowledge required to properly\nanalyze the data. In an experiment those analyzing the data either have or are working towards a\nPhD in physics. This is clearly not the average member of the public.\n\nThe details of how ATLAS and CMS responded to these challenges can and do differ but in\ngeneral the common approach is to provide data and tools at different levels of knowledge and\n\n1\n\n\n\nP\no\nS\n(\nI\nC\nH\nE\nP\n2\n0\n1\n6\n)\n3\n3\n5\n\nHEP data for everyone: CERN open data and the ATLAS and CMS experiments\nThomas McCauley for the ATLAS and CMS Collaborations\n\nexpertise required. This includes preparing and providing simplified datasets, detailed and com-\nprehensive documentation, example analysis code, easy-to-use and easy-to-access applications for\nanalysis and visualization via a web browser, and finally, for more advanced users, virtual machines\nwith the necessary software environment and access to the more complex analysis-level datasets.\n\n3. Data releases\n\n3.1 CERN Open Data Portal\n\nCERN, in collaboration with the LHC experiments, has prepared the CERN Open Data Portal\n(CODP) [6] from which the data and tools for the public are available. The portal is divided into\ntwo main areas, reflecting two different levels of access and complexity. The idea is to include and\nbuild upon the previous success of public data in education and outreach but also to include the\npossibility for more in-depth, complex analysis. The portal is therefore divided into two sections:\n\"Education\" and \"Research\". Datasets are distinguished as either \"primary\" or \"derived\" (roughly\nfalling into Level 3 and Level 2 categories, respectively)\n\nThe CODP is built with Invenio [7] digital library software which provides document organ-\nisation, search capability, and handling of metadata. The CODP relies on CERN support and\nservices for legacy data storage, access to and distribution of the data, and security and bandwidth\nrestrictions for public access. All four LHC experiments use the CODP to various extents; CMS at\nthis point uses it most extensively and the following description of the CMS data and tools release\nmakes frequent reference to the CODP.\n\n3.2 CMS\n\nThe CMS open data and tools release intends to reach all levels of the public and therefore\nthere is a spectrum of levels of access and complexity. In addition CMS addresses issues of data\ncataloging, validation, and preservation.\n\nCMS has released two large datasets to the public. The first, in November 2014 was half of\nthe 2010 proton-proton collision data at\n\n√\ns = 7 TeV, 27 TB in size, equivalent to an integrated\n\nluminosity of tens of pb−1. The second, in April 2016, was half of 2011 proton-proton collision\ndata at\n\n√\ns = 7 TeV, 100 TB in size, equivalent to around 2.5 fb−1, along with 200 TB of Monte\n\nCarlo samples. These datasets correspond to Level 3 described in Section 2 and are released in\nCMS AOD (Analysis Object Data) [8] ROOT [9] format. The data is released under the Creative\nCommons CC0 waiver [10], essentially releasing it into the public domain. In addition the data is\nidentified with persistent digital object identifiers (DOIs) and it is expected that third parties will\ncite the CMS public data through these identifiers. An image of some of the information included\nin a primary dataset record is shown in Figure [1].\n\nIn order to analyze the primary datasets one needs CMS software, access to the data, and the\ncorrect software environment. These are all provided via a virtual machine particular for each data\nrelease. The datasets are stored in EOS [11] and can be downloaded directly or accessed remotely\nin the virtual machines via XRootD [12]. Extensive documentation includes trigger and detector\ncondition information as well as analysis code examples. A set of benchmark analyses which can\nbe repeated using the public data are also available. They reproduce chosen measurements from\n\n2\n\n\n\nP\no\nS\n(\nI\nC\nH\nE\nP\n2\n0\n1\n6\n)\n3\n3\n5\n\nHEP data for everyone: CERN open data and the ATLAS and CMS experiments\nThomas McCauley for the ATLAS and CMS Collaborations\n\nFigure 1: A screenshot of a dataset record showing the dataset name, citation, and DOI\n\nFigure 2: A screenshot of the browser-based event display for interactive visualisation of CMS events in the\nCERN Open Data Portal\n\npublished CMS papers and can therefore validate the public data and serve as examples for further\nanalysis. Work is ongoing to provide a validation benchmark analysis for each primary dataset.\nThis all comprises the content in the \"Research\" section of the CODP for CMS.\n\nContent in the \"Education\" includes derived (i.e. reduced) datasets, corresponding to Level\n2 in csv (comma-separated-value), JSON (JavaScript Object Notation), and CMS PAT (Physics\nAnalysis Tool) [8] ROOT format. Also available in the \"Education\" section are two applications\nembedded in the CODP for easy interactive visualisation and examination of the datasets: an event\ndisplay [13] and a histogram application [14]. A screenshot of the event display is shown in Figure\n[2].\n\n3.3 ATLAS\n\nPrevious ATLAS data releases have included simplified datasets such as those used in the mas-\nterclasses and Monte-Carlo samples used for the ATLAS Higgs Machine Learning Challenge [15].\nIn July 2016 ATLAS released a large sample of data and tools with an initial focus on undergrad-\nuate and postgraduate students. Within this scope ATLAS provides access at three levels: from\nvisualization, to analysis via a web browser, up to more complex analysis. At each level extensive\ndocumentation and examples are provided. The data, tools, and documentation are available via\nthe ATLAS Open Data Page [16].\n\n3\n\n\n\nP\no\nS\n(\nI\nC\nH\nE\nP\n2\n0\n1\n6\n)\n3\n3\n5\n\nHEP data for everyone: CERN open data and the ATLAS and CMS experiments\nThomas McCauley for the ATLAS and CMS Collaborations\n\nFigure 3: A screenshot of the interactive histogram tool found in the ATLAS Open Data Page.\n\nFigure 4: The distribution of the the W transverse mass which can be determined from an advanced ATLAS\nopen data analysis.\n\nThe ATLAS dataset corresponds to 1 fb−1 of 2012 proton-proton collision data at\n√\n\ns = 8\nTeV along with accompanying Monte Carlo. The data is in ROOT TTree format and contain\nelectron/gamma and muon information. The first level of access with the ATLAS Open Data Page\nconsists of an interactive histogram tool and a ROOT file browser. A screenshot of the histogram\ntool is shown in Figure [3]. At the next level interactive Jupyter [17] notebooks are available [18]\nwhere one can analyse the data interactively using the C++ or Python languages. Finally at the\nlast, most-advanced, level [19] virtual machines with the environment and software necessary for\nanalysis are available. An example analysis includes a WZ analysis where one looks for both a W\nboson candidate and Z boson candidate. An example result, the distribution of the transverse mass\nof the W, can be seen in Figure [4].\n\nThe intention is that the data and tools (including the virtual machine) at about 14 GB in size\nshould all fit on a portable storage device such as a USB stick. This therefore allows for the data\nand tools to be distributed to the widest-possible audience where even perhaps online access is an\nissue. Data and virtual machines are also made available via CERN Open Data Portal.\n\n4\n\n\n\nP\no\nS\n(\nI\nC\nH\nE\nP\n2\n0\n1\n6\n)\n3\n3\n5\n\nHEP data for everyone: CERN open data and the ATLAS and CMS experiments\nThomas McCauley for the ATLAS and CMS Collaborations\n\n4. Conclusions and future plans\n\nBoth ATLAS and CMS have released data to the public in a continuing effort to encourage\npublic engagement, public education, and science. In order to maximize the potential of these data\nboth experiments have prepared and released extensive tools and documentation and made them\navailable online. Future plans include continuing release of data and improvement of data analysis\ntools and documentation. The goal will continue to be to ensure that as many as possible are able\nto use, learn from, and enjoy the public data.\n\n5. Acknowledgements\n\nWe wish to acknowledge the support of the ATLAS and CMS Collaborations and their com-\nmitments to open access and gratefully acknowledge the support of CERN Scientific Information\nServices and the Invenio team. We also wish to thank the conference organisers for an especially\nengaging education and outreach program.\n\nReferences\n\n[1] ATLAS Collaboration, JINST 3 S08003 (2008), http://atlas.cern\n\n[2] CMS Collaboration, JINST 3 S08004 (2008), http://cern.ch/cms\n\n[3] ATLAS Collaboration (2014). ATLAS Data Access Policy. CERN Open Data Portal. DOI:\n10.7483/OPENDATA.ATLAS.T9YR.Y7MZ\n\n[4] CMS Collaboration (2012). CMS data preservation, re-use and open access policy. CERN Open Data\nPortal. DOI: 10.7483/OPENDATA.CMS.UDBF.JKR9\n\n[5] http://physicsmasterclasses.org\n\n[6] http://opendata.cern.ch\n\n[7] http://invenio-software.org, http://urn.fi/URN:NBN:fi-fe2014070432236\n\n[8] A Hinzmann, Tools for Physics Analysis in CMS, J.Phys.Conf.Ser. 331 (2011) 032042.\n\n[9] R Brun, F Rademakers, ROOT - An Object Oriented Data Analysis Framework, Nucl. Inst. and Meth.\nin Phys. Res. A 389 (1997) 81, http://root.cern.ch\n\n[10] http://creativecommons.org/publicdomain/zero/1.0\n\n[11] http://information-technology.web.cern.ch/services/eos-service\n\n[12] http://xrootd.org\n\n[13] http://opendata.cern.ch/visualise/events/CMS\n\n[14] http://opendata.cern.ch/visualise/histograms/CMS\n\n[15] ATLAS Collaboration (2014). Dataset from the ATLAS Higgs Boson Machine Learning Challenge\n2014. CERN Open Data Portal. DOI: 10.7483/OPENDATA.ATLAS.ZBP2.M5T8\n\n[16] http://atlasopendata.web.cern.ch\n\n[17] http://jupyter.org/\n\n[18] http://atlas-opendata.web.cern.ch/atlas-opendata/webanalysis/\n\n[19] http://atlas-opendata.web.cern.ch/atlas-opendata/extendedanalysis/\n\n5"},"fulltext":true,"source":"PoS","key":"1d83f3dd3ba742f6bfb1836a75400767","url":"https://inspirehep.net/files/1d83f3dd3ba742f6bfb1836a75400767"}],"citation_count":3,"citation_count_without_self_citations":3,"report_numbers":[{"value":"CMS-CR-2016-329"}],"publication_info":[{"journal_volume":"ICHEP2016","artid":"335","conference_record":{"$ref":"https://inspirehep.net/api/conferences/1357486"},"year":2016,"journal_record":{"$ref":"https://inspirehep.net/api/journals/1213080"},"page_start":"335","journal_title":"PoS","parent_record":{"$ref":"https://inspirehep.net/api/literature/1507019"},"cnum":"C16-08-03"}],"authors":[{"raw_affiliations":[{"value":"University of Notre Dame (US)"}],"full_name_unicode_normalized":"mccauley, thomas","full_name":"McCauley, Thomas","curated_relation":true,"record":{"$ref":"https://inspirehep.net/api/authors/998129"},"last_name":"McCauley","affiliations":[{"record":{"$ref":"https://inspirehep.net/api/institutions/1224839"},"value":"U. 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pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$. Blue histograms are obtained from the full simulation and reconstruction chain; red ones are obtained using the generative model; gray histograms show the quantities obtained before detector simulation.","source":"arxiv","key":"881eb99787919603275aea8c9dc53862","url":"https://inspirehep.net/files/881eb99787919603275aea8c9dc53862"},{"filename":"scatter_pred.png","caption":"Joint distribution of the particle-level (``gen'') and reconstruction level pixel intensities. The left plot shows the distribution of the input data, while the right plot is obtained using the generative model.","source":"arxiv","key":"711fa56406b8dc19288eeb06c1087aa4","url":"https://inspirehep.net/files/711fa56406b8dc19288eeb06c1087aa4"},{"filename":"fig_scatter_pred.png","caption":"Joint distribution of the particle-level (``gen'') and reconstruction level pixel intensities. The left plot shows the distribution of the input data, while the right plot is obtained using the generative model.","source":"arxiv","key":"6700982b8c824c392fbc4ab28ee55530","url":"https://inspirehep.net/files/6700982b8c824c392fbc4ab28ee55530"},{"filename":"pt_dep_pred_4_gen.png","caption":"Evolution of the aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$ as a function of the particle level jet transverse momentum. Solid lines represent the median of the distribution, filled regions show the inter-quartile range, while dashed lines mark the 10\\% and 90\\% quantiles. Blue histograms are obtained from the full simulation and reconstruction chain; red ones are obtained using the generative model; gray histograms show the quantities obtained before detector simulation.","source":"arxiv","key":"1490064f23955833467dc3f103d76988","url":"https://inspirehep.net/files/1490064f23955833467dc3f103d76988"},{"filename":"pt_dep_pred_1_gen.png","caption":"Evolution of the aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$ as a function of the particle level jet transverse momentum. Solid lines represent the median of the distribution, filled regions show the inter-quartile range, while dashed lines mark the 10\\% and 90\\% quantiles. Blue histograms are obtained from the full simulation and reconstruction chain; red ones are obtained using the generative model; gray histograms show the quantities obtained before detector simulation.","source":"arxiv","key":"306acbc2c10ca08e0aa18d04d7bab5c5","url":"https://inspirehep.net/files/306acbc2c10ca08e0aa18d04d7bab5c5"},{"filename":"pt_dep_pred_1_delphes.png","caption":"Evolution of the aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$ as a function of the particle level jet transverse momentum. Solid lines represent the median of the distribution, filled regions show the inter-quartile range, while dashed lines mark the 10\\% and 90\\% quantiles. Blue lines are obtained from the input data; red ones are obtained using the generative model; gray ones using the gaussian-smearing model.","source":"arxiv","key":"704d0c47f381079f7f47335d2b7add12","url":"https://inspirehep.net/files/704d0c47f381079f7f47335d2b7add12"},{"filename":"sum_pred_1_gen.png","caption":"Aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$. Blue histograms are obtained from the full simulation and reconstruction chain; red ones are obtained using the generative model; gray histograms show the quantities obtained before detector simulation.","source":"arxiv","key":"7c6a582617f0f30cba188f1e87a83a22","url":"https://inspirehep.net/files/7c6a582617f0f30cba188f1e87a83a22"},{"filename":"pt_dep_pred_3.png","caption":"Evolution of the aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$ as a function of the particle level jet transverse momentum. Solid lines represent the median of the distribution, filled regions show the inter-quartile range, while dashed lines mark the 10\\% and 90\\% quantiles. Blue lines are obtained from the input data, while red ones are obtained using the generative model.","source":"arxiv","key":"232be7c28b6bb07b7ce910a1e2114bda","url":"https://inspirehep.net/files/232be7c28b6bb07b7ce910a1e2114bda"},{"filename":"fig_sum_pred.png","caption":"Aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$. Blue histograms are obtained from the input data, while red ones are obtained using the generative model.","source":"arxiv","key":"50928bf499f812ae81a203830ffe4043","url":"https://inspirehep.net/files/50928bf499f812ae81a203830ffe4043"},{"filename":"fig_pt_dep_pred_0.png","caption":"Evolution of the aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$ as a function of the particle level jet transverse momentum. Solid lines represent the median of the distribution, filled regions show the inter-quartile range, while dashed lines mark the 10\\% and 90\\% quantiles. Blue lines are obtained from the input data, while red ones are obtained using the generative model.","source":"arxiv","key":"0b019ac578cbe909eb354d1475da3432","url":"https://inspirehep.net/files/0b019ac578cbe909eb354d1475da3432"},{"filename":"pt_dep_pred_0_gen.png","caption":"Evolution of the aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$ as a function of the particle level jet transverse momentum. Solid lines represent the median of the distribution, filled regions show the inter-quartile range, while dashed lines mark the 10\\% and 90\\% quantiles. Blue histograms are obtained from the full simulation and reconstruction chain; red ones are obtained using the generative model; gray histograms show the quantities obtained before detector simulation.","source":"arxiv","key":"da0f486ffad35218bfaa6d5f431fc951","url":"https://inspirehep.net/files/da0f486ffad35218bfaa6d5f431fc951"},{"filename":"fig_pt_dep_pred_2.png","caption":"Evolution of the aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$ as a function of the particle level jet transverse momentum. Solid lines represent the median of the distribution, filled regions show the inter-quartile range, while dashed lines mark the 10\\% and 90\\% quantiles. Blue lines are obtained from the input data, while red ones are obtained using the generative model.","source":"arxiv","key":"60605e04e74f79dc0d30940fa3637be4","url":"https://inspirehep.net/files/60605e04e74f79dc0d30940fa3637be4"},{"filename":"fig_pt_dep_pred_3.png","caption":"Evolution of the aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$ as a function of the particle level jet transverse momentum. Solid lines represent the median of the distribution, filled regions show the inter-quartile range, while dashed lines mark the 10\\% and 90\\% quantiles. 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Solid lines represent the median of the distribution, filled regions show the inter-quartile range, while dashed lines mark the 10\\% and 90\\% quantiles. Blue lines are obtained from the input data; red ones are obtained using the generative model; gray ones using the gaussian-smearing model.","source":"arxiv","key":"b33ae6ec36e97421114c4b99cfd88adc","url":"https://inspirehep.net/files/b33ae6ec36e97421114c4b99cfd88adc"},{"filename":"sum_pred_gen.png","caption":"Aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$. Blue histograms are obtained from the full simulation and reconstruction chain; red ones are obtained using the generative model; gray histograms show the quantities obtained before detector simulation.","source":"arxiv","key":"3b7e152410557c406afad795a510191a","url":"https://inspirehep.net/files/3b7e152410557c406afad795a510191a"},{"filename":"sum_pred_4_delphes.png","caption":"Aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$. Blue histograms are obtained from the input data; red ones are obtained using the generative model; gray histograms are obtained with a gaussian-smearing model.","source":"arxiv","key":"2bece4e4078640eb2d26cf5e38af8f2e","url":"https://inspirehep.net/files/2bece4e4078640eb2d26cf5e38af8f2e"},{"filename":"avg_img.png","caption":"Average jet images obtained at the particle-level (``gen''), after detector simulation and reconstruction (``reco''), and those predicted by the generative model (``pred'').","source":"arxiv","key":"d580279782bd99d4fadeace66e25b00f","url":"https://inspirehep.net/files/d580279782bd99d4fadeace66e25b00f"},{"filename":"sum_pred_3_delphes.png","caption":"Aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$. Blue histograms are obtained from the input data; red ones are obtained using the generative model; gray histograms are obtained with a gaussian-smearing model.","source":"arxiv","key":"3e4633082a32a6f44c91b3335aaef075","url":"https://inspirehep.net/files/3e4633082a32a6f44c91b3335aaef075"},{"filename":"pt_dep_pred_3_delphes.png","caption":"Evolution of the aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$ as a function of the particle level jet transverse momentum. Solid lines represent the median of the distribution, filled regions show the inter-quartile range, while dashed lines mark the 10\\% and 90\\% quantiles. Blue lines are obtained from the input data; red ones are obtained using the generative model; gray ones using the gaussian-smearing model.","source":"arxiv","key":"195ccbed639f1ad484bf4b1bdd5de840","url":"https://inspirehep.net/files/195ccbed639f1ad484bf4b1bdd5de840"},{"filename":"pt_dep_metric_0.png","caption":"Evolution of the quark/gluon (first two rows) discrimination and merged jet tagging (last row) variables as a function of the particle level jet transverse momentum. Solid lines represent the median of the distribution, filled regions show the inter-quartile range, while dashed lines mark the 10\\% and 90\\% quantiles. Blue lines are obtained from the input data, while red ones are obtained using the generative model.","source":"arxiv","key":"66ea4d8ab31a29d7e1a8b5f882a999ad","url":"https://inspirehep.net/files/66ea4d8ab31a29d7e1a8b5f882a999ad"},{"filename":"pt_dep_pred_delphes.png","caption":"Evolution of the aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$ as a function of the particle level jet transverse momentum. Solid lines represent the median of the distribution, filled regions show the inter-quartile range, while dashed lines mark the 10\\% and 90\\% quantiles. Blue lines are obtained from the input data; red ones are obtained using the generative model; gray ones using the gaussian-smearing model.","source":"arxiv","key":"f6bdc4aa3e0d8be47e0133de97f425df","url":"https://inspirehep.net/files/f6bdc4aa3e0d8be47e0133de97f425df"},{"filename":"metric_1.png","caption":"Distribution of high level variables used for quark/gluon discrimination (first two rows) and merged jets tagging (last row). Blue histograms are obtained from the input data, while red ones are obtained using the generative model.","source":"arxiv","key":"9e464e67ac1ecb333092473a14011ba6","url":"https://inspirehep.net/files/9e464e67ac1ecb333092473a14011ba6"},{"filename":"sum_pred_2_delphes.png","caption":"Aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$. Blue histograms are obtained from the input data; red ones are obtained using the generative model; gray histograms are obtained with a gaussian-smearing model.","source":"arxiv","key":"dfc1e47808adef8e05da4c178c64b1e1","url":"https://inspirehep.net/files/dfc1e47808adef8e05da4c178c64b1e1"},{"filename":"sum_pred_2.png","caption":"Aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$. Blue histograms are obtained from the input data, while red ones are obtained using the generative model.","source":"arxiv","key":"681879d004904069f99612db041054ab","url":"https://inspirehep.net/files/681879d004904069f99612db041054ab"},{"filename":"gen_pt.png","caption":"Distribution of the jet transverse momentum at particle level for the hadronic jets in the input dataset. The blue histogram shows the distribution as coming from the dataset, while the red one shows the distribution obtained after applying the weights used for training.","source":"arxiv","key":"2780bb6782b46e747147c90f71c28c8a","url":"https://inspirehep.net/files/2780bb6782b46e747147c90f71c28c8a"},{"filename":"sum_pred_4_gen.png","caption":"Aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$. Blue histograms are obtained from the full simulation and reconstruction chain; red ones are obtained using the generative model; gray histograms show the quantities obtained before detector simulation.","source":"arxiv","key":"da6f18270dcb231c419229bae2192b1d","url":"https://inspirehep.net/files/da6f18270dcb231c419229bae2192b1d"},{"filename":"sum_pred_0_gen.png","caption":"Aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$. Blue histograms are obtained from the full simulation and reconstruction chain; red ones are obtained using the generative model; gray histograms show the quantities obtained before detector simulation.","source":"arxiv","key":"e2fefed4526aff4549aa99f6d39a921f","url":"https://inspirehep.net/files/e2fefed4526aff4549aa99f6d39a921f"},{"filename":"pt_dep_pred_gen.png","caption":"Evolution of the aggregated pixel intensities for different rings in $\\Delta \\eta$--$\\Delta \\phi$ as a function of the particle level jet transverse momentum. Solid lines represent the median of the distribution, filled regions show the inter-quartile range, while dashed lines mark the 10\\% and 90\\% quantiles. Blue histograms are obtained from the full simulation and reconstruction chain; red ones are obtained using the generative model; gray histograms show the quantities obtained before detector simulation.","source":"arxiv","key":"c20d73c706660da635c871a33186320f","url":"https://inspirehep.net/files/c20d73c706660da635c871a33186320f"},{"filename":"metric_2.png","caption":"Distribution of high level variables used for quark/gluon discrimination (first two rows) and merged jets tagging (last row). Blue histograms are obtained from the input data, while red ones are obtained using the generative model.","source":"arxiv","key":"644e9977cf72f003a3c7d5a44725b630","url":"https://inspirehep.net/files/644e9977cf72f003a3c7d5a44725b630"},{"filename":"metric_3.png","caption":"Distribution of high level variables used for quark/gluon discrimination (first two rows) and merged jets tagging (last row). 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In particular, generative adversarial networks provide an unsupervised solution to this problem. In this work, we apply this kind of technique to the simulation of particle detector response to hadronic jets. We show that deep neural networks can achieve high fidelity in this task, while attaining a speed increase of several orders of magnitude with respect to traditional algorithms.","abstract_source_suggest":{"input":"Springer"}},{"source":"arXiv","value":"Deep generative models parametrised by neural networks have recently started to provide accurate results in modelling natural images. In particular, generative adversarial networks provide an unsupervised solution to this problem. In this work we apply this kind of technique to the simulation of particle-detector response to hadronic jets. We show that deep neural networks can achieve high-fidelity in this task, while attaining a speed increase of several orders of magnitude with respect to traditional algorithms.","abstract_source_suggest":{"input":"arXiv"}}],"primary_arxiv_category":["hep-ex"],"titles":[{"source":"Springer","title":"Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks"},{"source":"arXiv","title":"Fast and accurate simulation of particle detectors using generative adversarial neural networks"},{"source":"arXiv","title":"Fast and accurate simulation of particle detectors using generative adversarial networks"}],"imprints":[{"date":"2018-11-02"}],"curated":true}}],"total":136},"links":{"self":"https://inspirehep.net/api/literature/?q=references.reference.dois%3A10.7483%2FOPENDATA.CMS%2A&size=10&page=1","next":"https://inspirehep.net/api/literature/?q=references.reference.dois%3A10.7483%2FOPENDATA.CMS%2A&size=10&page=2","bibtex":"https://inspirehep.net/api/literature/?q=references.reference.dois%3A10.7483%2FOPENDATA.CMS%2A&size=10&page=1&format=bibtex","latex-eu":"https://inspirehep.net/api/literature/?q=references.reference.dois%3A10.7483%2FOPENDATA.CMS%2A&size=10&page=1&format=latex-eu","latex-us":"https://inspirehep.net/api/literature/?q=references.reference.dois%3A10.7483%2FOPENDATA.CMS%2A&size=10&page=1&format=latex-us","json":"https://inspirehep.net/api/literature/?q=references.reference.dois%3A10.7483%2FOPENDATA.CMS%2A&size=10&page=1&format=json","json-expanded":"https://inspirehep.net/api/literature/?q=references.reference.dois%3A10.7483%2FOPENDATA.CMS%2A&size=10&page=1&format=json-expanded","cv":"https://inspirehep.net/api/literature/?q=references.reference.dois%3A10.7483%2FOPENDATA.CMS%2A&size=10&page=1&format=cv"}}