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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","last_name":"Perez Adan","ids":[{"schema":"ORCID","value":"0000-0003-3416-0726"},{"schema":"INSPIRE BAI","value":"Danyer.Perez.Adan.1"}],"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":"2019-12-11T00:00:00+00:00","updated":"2023-03-07T02:57:44.077596+00:00","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"},"id":"1769970","metadata":{"citation_count_without_self_citations":4,"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. 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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","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"},"id":"1413825","metadata":{"report_numbers":[{"value":"FERMILAB-CONF-16-081-CD"}],"citation_count_without_self_citations":2,"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,"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 - 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Cari","last_name":"Cesarotti","ids":[{"schema":"ORCID","value":"0000-0001-5128-7919"},{"schema":"INSPIRE BAI","value":"C.Cesarotti.1"}],"first_name":"Cari","recid":1793525},"control_number":2730282,"earliest_date":"2022-05","document_type":["thesis"],"texkeys":["Cesarotti:2022ebp"],"abstracts":[{"source":"Harvard U. (main)","value":"Despite extensive theoretical and experimental efforts, we have yet to discover evidence of new physics. Since there is no obvious candidate for a particular new physics model that can resolve the variety of anomalies and shortcomings of the Standard Model, a potentially lucrative strategy is to explore as broadly as possible. In this dissertation we consider a two pronged approach to this strategy: constructing phenomenological models that capture the dynamics of general new physics scenarios, and developing robust tools for identifying new phenomena. We consider primarily the application of this strategy at existing and future colliders. For phenomenological models, we first consider the generic scenario where new particles are produced back-to-back with substantial transverse momenta, ultimately decaying into a dimuon pair. We also use extra dimensional models to construct tractable hidden sector scenarios with tunable radiation patterns. For both of these toy models, we expect signal events to look inherently distinct from Standard Model backgrounds. To classify these signatures, we develop a new event shape observable—event isotropy—that has a complementary dynamic range to commonly used event shapes. We discuss the efficacy of this observable and explore potential correlations. Finally we discuss how this program for new physics searches can expand even further with the construction of a multi-TeV muon machine. We show how using muons increases sensitivity to new physics scenarios that couple to second-generation particles and how we can use an O(TeV) beam in a beam-dump experiment to probe enormously small couplings. Ultimately we present many possible channels towards uncovering the first hints of new physics to motivate more concentrated efforts to identify the particle content of the universe.","abstract_source_suggest":{"input":"Harvard U. (main)"}}],"persistent_identifiers":[{"schema":"URN","source":"Harvard U. (main)","value":"URN-3:HUL.INSTREPOS:37372191"}],"titles":[{"source":"Harvard U. (main)","title":"Hints of a Hidden World"}],"facet_author_name":["1793525_Cari Cesarotti"],"core":true,"imprints":[{"date":"2022-05"}],"_oai":{"sets":["Literature"],"id":"oai:inspirehep.net:2730282","updated":"2024-09-26T17:58:56.150949"},"thesis_info":{"date":"2022","institutions":[{"curated_relation":true,"record":{"$ref":"https://inspirehep.net/api/institutions/921401"},"name":"Harvard U. (main)"}],"degree_type":"PhD"},"curated":true,"supervisors":[{"full_name":"Reece, Matthew","record":{"$ref":"https://inspirehep.net/api/authors/1025278"},"last_name":"Reece","ids":[{"schema":"INSPIRE BAI","value":"M.Reece.1"}],"inspire_roles":["supervisor"],"first_name":"Matthew","uuid":"ce5e4005-e5a4-4289-993b-1fc9a6e63288","recid":1025278}]}},{"created":"2021-04-23T00:00:00+00:00","updated":"2025-01-23T19:13:15.414149+00:00","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"},"id":"1860231","metadata":{"citation_count_without_self_citations":1,"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,"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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Numba: A llvm-based 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."}]}},{"reference":{"urls":[{"value":"https://developer.nvidia.com/cuda-zone"}],"label":"7","misc":["Nvidia CUDA Home page"]}},{"reference":{"collaborations":["CMS collaboration"],"dois":["10.7483/OPENDATA.CMS.42GY.2VJI"],"label":"8","publication_info":{"year":2017},"misc":["Simulated datasets in AODSIM format for 2012 collision data. CERN Open Data Portal. 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Newman","1023557_Jean-Roch Valery Vlimant","1077949_Joosep Pata","1239742_Nan Lu","1496893_Daniele Ruini","1622349_Irene Dutta","1671459_Christina Reissel"],"core":true,"license":[{"license":"CC-BY-NC-ND-4.0","imposing":"SISSA"}],"_oai":{"sets":["Literature"],"id":"oai:inspirehep.net:1860231","updated":"2025-01-23T19:13:15.414149"},"journal_title_variants":["PoS"],"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"],"legacy_version":"20210520094703.0","inspire_categories":[{"term":"Computing"}],"first_author":{"emails":["jpata@caltech.edu"],"full_name":"Pata, Joosep","last_name":"Pata","first_name":"Joosep","recid":1077949},"control_number":1860231,"dois":[{"source":"SISSA","value":"10.22323/1.390.0908"}],"document_type":["conference paper"],"texkeys":["Pata:2021sud","Dutta:2021esn"],"abstracts":[{"source":"SISSA","value":"At HEP experiments, processing billions of records of structured numerical data can be a bottleneckin the analysis pipeline. This step is typically more complex than current query languages allow,such that numerical codes are used. As highly parallel computing architectures are increasinglyimportant in the computing ecosystem, it may be useful to consider how accelerators such as GPUscan be used for data analysis. Using CMS and ATLAS Open Data, we implement a benchmarkphysics analysis with GPU acceleration directly in Python based on efficient computational kernelsusing Numba/LLVM, resulting in an order of magnitude throughput increase over a pure CPU-based approach. We discuss the implementation and performance benchmarks of the physicskernels on CPU and GPU targets. We demonstrate how these kernels are combined to a modernML-intensive workflow to enable efficient data analysis on high-performance servers and remarkon possible operational considerations.","abstract_source_suggest":{"input":"SISSA"}}],"titles":[{"source":"SISSA","title":"Data Analysis with GPU-Accelerated Kernels"}],"imprints":[{"date":"2021-03-02","publisher":"SISSA"}],"curated":true}},{"created":"2020-11-23T00:00:00+00:00","updated":"2023-03-07T02:22:51.821710+00:00","links":{"bibtex":"https://inspirehep.net/api/literature/1832119?format=bibtex","latex-eu":"https://inspirehep.net/api/literature/1832119?format=latex-eu","latex-us":"https://inspirehep.net/api/literature/1832119?format=latex-us","json":"https://inspirehep.net/api/literature/1832119?format=json","json-expanded":"https://inspirehep.net/api/literature/1832119?format=json-expanded","cv":"https://inspirehep.net/api/literature/1832119?format=cv","citations":"https://inspirehep.net/api/literature/?q=refersto%3Arecid%3A1832119"},"id":"1832119","metadata":{"report_numbers":[{"value":"CMS-CR-2020-025"}],"citation_count_without_self_citations":0,"documents":[{"filename":"10.1051_epjconf_202024508006.pdf","attachment":{"content":"Using CMS Open Data for education, outreach and soft-\nware development\n\nStefan Wunsch1,2,∗\n\n1CERN\n2Karlsruhe Institute of Technology\n\nAbstract. The CMS collaboration at the CERN LHC has made more than one\npetabyte of open data available to the public, including large parts of the data\nwhich formed the basis for the discovery of the Higgs boson in 2012. Apart\nfrom their scientific value, these data can be used not only for education and\noutreach, but also for software development. However, in their original for-\nmat, the data cannot be accessed easily without experiment-specific knowledge\nand skills. Work is presented that allows to set up open analyses that are per-\nformed close to the published ones, but which meet minimum requirements for\nexperiment-specific knowledge and software. The suitability of this approach\nfor education and outreach is demonstrated with analyses that have been made\nfully accessible to the public via the CERN Open Data portal. Further, the value\nof these data for software development and as basis for benchmarks of analy-\nsis software under realistic conditions of a high-energy physics experiment is\ndiscussed.\n\n1 Introduction\n\nThe CMS collaboration has recently published on the CERN Open Data portal [1] a new\nbatch of open data to the public [2]. The release increases the volume of the open data to\nmore than two petabyte including large parts of the data used for the discovery of the Higgs\nboson in 2012. In the future, we can expect a continuous growth of these resources due\nto the CMS Open Data policy [3], which states that the collaboration commits to releasing\n100 % of its analysable data within ten years of collecting them, making CMS Open Data\nan invaluable resource for open science. All other large LHC experiments have published\nsimilar statements [4–6]. The CMS Open Data releases are already today basis of scientific\npublications, naturally in the field of particle physics [7, 8] but as well in studies related to\ndata science and machine learning [9, 10].\n\nBesides the purely scientific use-cases, open data is also valuable for education and out-\nreach being already actively used around the world [11, 12], especially for attracting young\npeople to CMS and high-energy physics. This paper presents an approach to ease the usage\nof open data for education and outreach and points out the additional value of such resources\nfor software development in high-energy physics.\n\nSection 2 shows in which data-format most of the CMS Open Data is currently distributed\nand discusses which aspects are most important for facilitating its use. Section 3 presents\n∗e-mail: stefan.wunsch@cern.ch\n\n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons \nAttribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).\n\nEPJ Web of Conferences 245, 08006 (2020)\nCHEP 2019\n\nhttps://doi.org/10.1051/epjconf/202024508006\n\n\n\nexamples for education and outreach using these accessible resources and section 4 puts\nemphasis on the impact and importance of such accessible data on software development.\n\n2 Data-format of CMS Open Data\n\nIn the current release of the CMS Open Data, most of the collision data and simulated data are\npublished in a data-format known as AOD (Analysis Object Data). This data-format consists\nof serialized C++ objects, which requires experiment-specific software (CMSSW [13]) and\nROOT [14] to be read. Further, each event holds about 500 kB/event of information resulting\nin general in large files. AOD is a powerful but complex data-format, which does not fit the\nneeds of the here discussed use-cases of the data.\n\nThe CMS collaboration has developed derived data-formats called MiniAOD [15] and its\nsuccessor NanoAOD [16]. Each format reduces the information content so that the size per\nevent is reduced by an order of magnitude each, resulting in about 2 kB/event for NanoAOD\nfiles. Whereas MiniAOD is still similar to AOD and stores serialized C++ objects, NanoAOD\nis based on the storage of basic types such as floats, integers and arrays thereof. Table 1 shows\nas example the layout of a muon collection in the NanoAOD data-format.\n\nTable 1. Data-format of a muon collection in the NanoAOD format\n\nVariable Type Description\nnMuon unsigned int Number of muons in this event\nMuon_pt float[nMuon] Transverse momentum of the muons\nMuon_eta float[nMuon] Pseudorapidity of the muons\nMuon_phi float[nMuon] Azimuth of the muons\nMuon_mass float[nMuon] Mass of the muons\nMuons_charge int[nMuon] Charge of the muons (either 1 or -1)\n\nTherefore, NanoAOD is readable independent from experiment-specific software with\nany library capable to read ROOT files. The data-format of NanoAOD satisfies all require-\nments for the emphasized use-cases, which are:\n\n• Simple data-format\n\n• Readable without experiment-specific software\n\n• Small files\n\nMoreover, the data-format is in use by actual CMS analyses, which facilitates the usage for\neducation and outreach by being close to actual research and provides a perfect playground\nfor studying analysis workflows.\n\nFor these reasons, a conversion tool for the AOD data-format to a reduced NanoAOD\nformat was developed in order to make the CMS Open Data release more accessible [17].\nThe conversion tool targets the conversion of AOD files taken in 2012 with a total recorded\nintegrated luminosity of 21.8 fb−1 [18]. At the time of the development only half of the data\ntaken in 2012 was published (Run B and C) so that the examples in sections 3 and 4 are\nbased on an integrated luminosity of about 11.5 fb−1. It should be noted that the content of\nthe reduced CMS Open Data NanoAOD files is not validated and therefore must not be used\nfor any physics measurements.\n\n2\n\nEPJ Web of Conferences 245, 08006 (2020)\nCHEP 2019\n\nhttps://doi.org/10.1051/epjconf/202024508006\n\n\n\n3 Usage for education and outreach\n\nPhysics analysis examples for education and outreach can be separated roughly in two cate-\ngories:\n\n• High-school level: Students or the general public who have the first contact with high-\nenergy particle physics and little knowledge about programming and data analysis\n\n• University level: Students or individuals studying advanced particle physics with existent\nknowledge about programming and data analysis\n\nIn the following, two examples are presented, each serving one of the categories above.\n\n3.1 Analysis of the di-muon spectrum\n\nThis high-school level example consists in the analysis of the di-muon spectrum. The input\nfile is in the reduced NanoAOD format as described above, generated from the validated runs\nof the double muon primary dataset and contains only the muon collection such as shown in\ntable 1. The full information about the data is transparently documented in the record on the\nportal [19]. The conversion and filtering reduces the AOD files with a size of 19.4 TB to a\ndataset with only 2.2 GB while keeping about 61.5 million events.\n\nThe analysis selects events with exactly two muons, checks for opposite-charged pairs and\neventually computes the invariant mass of the di-muon system and makes a histogram. The\nresult can be seen in figure 1. A reference implementation is provided in C++ and Python\nusing only ROOT with in total less than 100 lines of code. It has an approximate runtime\nof one minute on a consumer laptop with an SSD running on a single thread. It should be\nnoted that the analysis is trivially parallelizable, which is also supported by the reference\nimplementation powered by the RDataFrame facility from ROOT [20].\n\nFigure 1. Analysis of the di-muon spectrum using data from the CMS detector taken in 2012 [19].\n\nThe analysis illustrates the concept of particle resonances and an invariant mass and fi-\nnally enables the user to rediscover particle resonances in a wide energy range from the η\nmeson at about 548 MeV up to the Z boson at about 91 GeV.\n\n3\n\nEPJ Web of Conferences 245, 08006 (2020)\nCHEP 2019\n\nhttps://doi.org/10.1051/epjconf/202024508006\n\n\n\n3.2 Analysis of Higgs boson decays to two tau leptons\n\nThis example uses data and simulation of events analyzing 11.5 fb−1 with the goal to study\ndecays of a Higgs boson into two tau leptons in the final state of a muon lepton and a ha-\ndronically decayed tau lepton. The analysis follows loosely the setup of the official CMS\nanalysis published in 2014 [21]. The purpose of the original CMS analysis was to establish\nthe existence of the Higgs boson decaying into two tau leptons. Since performing this analysis\nproperly with full consideration of all systematic uncertainties is an highly complex task, we\nreduce this analysis to the qualitative study of the kinematics and properties of such events\nwithout a statistical analysis. However, as you can find in the following, already such a\nreduced analysis is complex and requires to understand an extensive physics context, which\nmakes this a perfect first look into the procedures required to claim the evidence or existence\nof new particles.\n\nThe analysis processes data, two signal samples representing a Higgs boson produced\nby gluon-fusion and vector-boson fusion, and the four most dominant background processes\nas simulated events. In addition, the QCD multijet contribution is estimated with a data-\ndriven technique via an extrapolation from a control region with tau candidates of the same\ncharge. Full details can be found in the record on the portal [22]. The analysis processes\nnine different datasets for a total of 69.3 GB consisting of 87 million recorded events and 114\nmillion simulated events. The workflow follows a typical analysis at CMS:\n\n1. The NanoAOD files are preprocessed and reduced to the event information needed for\nthis analysis. In addition, a pair selection is performed to find from the muon and tau\ncollections the pair which originates most likely from a Higgs boson. The result is a flat\nanalysis dataset for further processing. The runtime on a consumer laptop and locally\nread files is around twenty minutes.\n\n2. The flat analysis dataset is processed to compute histograms of 34 observables and all\ncontributing processes. Further, similar histograms have to be produced in a control\nregion for the data-driven QCD estimation, which sums up in total to multiple hun-\ndreds of histograms representing all processes and observables. The runtime for the\nprocessing is in the order of one minute.\n\n3. The histograms are combined to physically meaningful plots. For example, figure 2\nshows the visible mass of the di-tau system. The runtime of this step with less than a\nfew seconds is negligible.\n\nThe analysis allows to study the contribution of the different physics processes to the data\ntaken with the CMS detector. The complexity level is close to an actual CMS analysis with a\nrich physics content including complex objects such as jets and taus.\n\n4 Usage for software development\n\nSoftware development in high-energy physics is currently very active in studying analysis\nworkflows of the future, see for example the efforts in the HEP Software Foundation [23].\nHowever, since the developments are driven from inside the experiment collaborations, any\nanalysis examples using new developed tools take typically experiment datasets as input. The\ndatasets are usually subject to the collaboration policies, which prevents free sharing of the\ndatasets outside of the collaboration. The lack of realistic and openly accessible analyses\nbeyond over-simplified examples for education and outreach impedes the progress in this\neffort since freely shareable one-to-one comparison of different analysis tools are complicated\n\n4\n\nEPJ Web of Conferences 245, 08006 (2020)\nCHEP 2019\n\nhttps://doi.org/10.1051/epjconf/202024508006\n\n\n\n20 40 60 80 100 120 140\n\nVisible di-tau mass / GeV\n\n2000\n\n4000\n\n6000\n\n8000\n\n10000\n\n12000E\nve\n\nnt\ns\n\nN\n\nττ→Z ll→Z\nW+jets tt\nQCD multijet H (x10)→gg\n\nH (x100)→qq Data\n\n (2012, 8 TeV)-111.5 fbCMS Open Data\n\nFigure 2. Analysis of Higgs boson decays to two tau leptons using data and simulation of events at the\nCMS detector from 2012 [22].\n\nto put in place. Further, software in high-energy physics is usually open source. However,\nthe lack of open analysis examples do not allow to publish comprehensive analysis examples\nwith the software since the data is protected.\n\nFor these use-cases, the resources presented in section 3 are well suited for several rea-\nsons:\n\n• High accessibility of the resources minimizes the initial effort to run the examples.\n\n• The datasets are open and free from experiment-specific software.\n\n• The NanoAOD format is a lightweight and easily readable data format.\n\n• Software examples and analyses for education and outreach gain from a realistic setting\nbut usually do not require the same precision as for a physics measurement.\n\nThe examples presented in section 3 have been used successfully for software tutorials,\nhands-on teaching and for software benchmarking of the ROOT framework [14, 24]. All\neducational material and benchmark studies are fully open and reproducible, which facilitates\nlearning the usage of the software and promotes collaborative work for future developments.\n\n5 Summary\n\nThe paper introduces new resources that allow to set up analyses with open data from the\nCMS experiment, which require a minimum of experiment-specific knowledge and software.\nTwo example analyses are presented that are well suited for education and outreach on high-\nschool and university level but also have the properties to facilitate software development in\nhigh-energy physics. The similarities between the requirements of education and outreach\nand software development are demonstrated. Because the datasets are free from experiment-\nspecific software and any restrictions imposed by the collaborations, these examples are well\n\n5\n\nEPJ Web of Conferences 245, 08006 (2020)\nCHEP 2019\n\nhttps://doi.org/10.1051/epjconf/202024508006\n\n\n\nsuited to facilitate open discussions and inter-experimental collaborative work most notably\nin the field of software development and future analysis workflows.\n\nReferences\n\n[1] CERN, The CERN Open Data portal, http://opendata.cern.ch\n[2] The CMS collaboration, CMS releases open data for Machine Learning, http://\nopendata.cern.ch/docs/cms-releases-open-data-for-machine-learning\n\n[3] The CMS collaboration, CMS preservation policy, 10.7483/OPENDATA.CMS.7347.\nJDWH\n\n[4] The ATLAS collaboration, ATLAS preservation policy, 10.7483/OPENDATA.ATLAS.\nT9YR.Y7MZ\n\n[5] The LHCb collaboration, LHCb preservation policy, 10.7483/OPENDATA.LHCb.\nHKJW.TWSZ\n\n[6] The ALICE collaboration, ALICE preservation policy, 10.7483/OPENDATA.ALICE.\n54NE.X2EA\n\n[7] A. Tripathee, W. Xue, A. Larkoski, S. Marzani, J. Thaler, Jet substructure studies with\nCMS open data (2017), http://dx.doi.org/10.1103/PhysRevD.96.074003\n\n[8] C. Cesarotti, Y. Soreq, M.J. Strassler, J. Thaler, W. Xue, Searching in CMS open data for\ndimuon resonances with substantial transverse momentum (2019), http://dx.doi.\norg/10.1103/PhysRevD.100.015021\n\n[9] P. Musella, F. Pandolfi, Fast and Accurate Simulation of Particle Detectors\nUsing Generative Adversarial Networks (2018), http://dx.doi.org/10.1007/\ns41781-018-0015-y\n\n[10] A. Di Florio, F. Pantaleo, M. Pierini, Sample with tracker hit information for tracking\nalgorithm ML studies, 10.7483/OPENDATA.CMS.N1IN.TQHD\n\n[11] University of Central Florida, Physics Hosts Teaching & Data Workshops, https://\nsciences.ucf.edu/news/physics-hosts-teaching-data-workshops/\n\n[12] Helsinki Insitute of Physics, Education and Open Data, http://www.hip.fi/?page_\nid=2985\n\n[13] The CMS collaboration, CMS Offline Software, https://github.com/cms-sw/\ncmssw\n\n[14] R. Brun, F. Rademakers, ROOT - An object oriented data analysis framework (1997)\n[15] G. Petrucciani, A. Rizzi, C. Vuosalo, Mini-AOD: A New Analysis Data Format for CMS\n\n(2015), http://dx.doi.org/10.1088/1742-6596/664/7/072052\n[16] A. Rizzi, G. Petrucciani, M. Peruzzi (CMS Collaboration), EPJ Web Conf. 214, 06021.\n\n6 p (2019)\n[17] S. Wunsch, Tool for conversion of CMS AOD files to reduced NanoAOD format for the\n\npurpose of education and outreach (2019), 10.7483/OPENDATA.CMS.944Q.PN2X\n[18] The CMS collaboration, Public CMS Luminosity Information, https://twiki.cern.\n\nch/twiki/bin/view/CMSPublic/LumiPublicResults\n\n[19] S. Wunsch, Analysis of the di-muon spectrum using data from the CMS detector taken\nin 2012 (2019), 10.7483/OPENDATA.CMS.AAR1.4NZQ\n\n[20] E. Guiraud, A. Naumann, D. Piparo, TDataFrame: functional chains for ROOT data\nanalyses (2017), https://doi.org/10.5281/zenodo.260230\n\n[21] The CMS collaboration, Evidence for the 125 GeV Higgs boson decaying to a pair of\ntau leptons (2014), http://dx.doi.org/10.1007/JHEP05(2014)104\n\n6\n\nEPJ Web of Conferences 245, 08006 (2020)\nCHEP 2019\n\nhttps://doi.org/10.1051/epjconf/202024508006\n\n\n\n[22] S. Wunsch, Analysis of Higgs boson decays to two tau leptons using data and simulation\nof events at the CMS detector from 2012 (2019), 10.7483/OPENDATA.CMS.GV20.PR5T\n\n[23] The HEP Software Foundation, https://hepsoftwarefoundation.org/\n[24] V.E. Padulano, Blurring High Energy Physics Data Analysis Techniques and Data Sci-\n\nence Approaches (2019), presented 25 Oct 2019, http://cds.cern.ch/record/\n2693575\n\n7\n\nEPJ Web of Conferences 245, 08006 (2020)\nCHEP 2019\n\nhttps://doi.org/10.1051/epjconf/202024508006"},"fulltext":true,"key":"92e9c39878cf23e932d32b0893b45fec","url":"https://inspirehep.net/files/92e9c39878cf23e932d32b0893b45fec"}],"citation_count":0,"publication_info":[{"journal_volume":"245","artid":"08006","conference_record":{"$ref":"https://inspirehep.net/api/conferences/1732179"},"year":2020,"journal_record":{"$ref":"https://inspirehep.net/api/journals/1211782"},"journal_title":"EPJ Web Conf.","parent_record":{"$ref":"https://inspirehep.net/api/literature/1830716"},"cnum":"C19-11-04"}],"authors":[{"raw_affiliations":[{"value":"CERN"},{"value":"Karlsruhe Institute of Technology"}],"full_name_unicode_normalized":"wunsch, stefan","full_name":"Wunsch, 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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}}],"total":144},"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"}}