Elaborating Higgs to dimuon decay from gluon fusion by decorrelation and jet substructure - INSPIRE

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Elaborating Higgs to dimuon decay from gluon fusion by decorrelation and jet substructure

Jun 17, 2024

25 pages

e-Print:

2406.11961 [hep-ph]

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Abstract: (arXiv)

Discovery of the Higgs boson decay to dimuon is anticipated soon based on the current evidence. Precise categorization of the events without affecting the invariant mass shape is crucial in the analysis. Decorrelation of the invariant mass and the output of discriminators (the score of discriminators) is essential for consistent and precise analysis. In this paper we use distance correlation as the additional loss function to achieve the decorrelation for discriminators and examine various analysis methods. The analyses with and without jet substructure variables are presented. Adding jet substructure variables considerably improves the significance of the Higgs to dimuon signal from gluon fusion.

Note:

25 pages, 7 figures, 7 tables: submitted to PRD

References(39)

Figures(7)

[1]

Observation of a New Boson at a Mass of 125 GeV with the CMS Experiment at the LHC

CMS

Collaboration

•

Serguei Chatrchyan
(
- Yerevan Phys. Inst.
)

et al.

- Phys.Lett.B 716 (2012) 30-61
•
e-Print:
- 1207.7235
•
DOI:
- 10.1016/j.physletb.2012.08.021

[2]

Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC

ATLAS

Collaboration

•

Georges Aad
(
- Freiburg U.
)

et al.

- Phys.Lett.B 716 (2012) 1-29
•
e-Print:
- 1207.7214
•
DOI:
- 10.1016/j.physletb.2012.08.020

[3]

Combined measurements of Higgs boson production and decay using up to $80$ fb $^{-1}$ of proton-proton collision data at $\sqrt{s}=$ 13 TeV collected with the ATLAS experiment

ATLAS

Collaboration

•

Georges Aad
(
- Marseille, CPPM
)

et al.

- Phys.Rev.D 101 (2020) 1, 012002
•
e-Print:
- 1909.02845
•
DOI:
- 10.1103/PhysRevD.101.012002

[4]

Observation of Higgs boson production in association with a top quark pair at the LHC with the ATLAS detector

ATLAS

Collaboration

•

M. Aaboud

et al.

- Phys.Lett.B 784 (2018) 173-191
•
e-Print:
- 1806.00425
•
DOI:
- 10.1016/j.physletb.2018.07.035

[5]

Observation of Higgs boson decay to bottom quarks

CMS

Collaboration

•

A. M. Sirunyan

et al.

- Phys.Rev.Lett. 121 (2018) 12, 121801
•
e-Print:
- 1808.08242
•
DOI:
- 10.1103/PhysRevLett.121.121801

[6]

Cross-section measurements of the Higgs boson decaying into a pair of $\tau$ -leptons in proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

ATLAS

Collaboration

•

Morad Aaboud
(
- Oujda U.
)

et al.

- Phys.Rev.D 99 (2019) 072001
•
e-Print:
- 1811.08856
•
DOI:
- 10.1103/PhysRevD.99.072001

[7]

Evidence for Higgs boson decay to a pair of muons

CMS

Collaboration

•

Albert M Sirunyan
(
- Yerevan Phys. Inst.
)

et al.

- JHEP 01 (2021) 148
•
e-Print:
- 2009.04363
•
DOI:
- 10.1007/JHEP01(2021)148

[8]

Association, correlation and causation

- Nature Meth. 12 (2015) 899-900
•
DOI:
- 10.1038/nmeth.3587

[9]

Machine Learning Techniques in the CMS Search for Higgs Decays to Dimuons

CMS

Collaboration

•

Dimitri Bourilkov
(
- Florida U.
)

et al.

- EPJ Web Conf. 214 (2019) 06002
•
DOI:
- 10.1051/epjconf/201921406002

[10]

Learning to Pivot with Adversarial Networks

Gilles Louppe
(
- New York U. (main)
)
,
Michael Kagan
,
Kyle Cranmer
(
- New York U.
)

- Advances in Neural Information Processing Systems 30, pages 981-990, 2017
•
e-Print:
- 1611.01046

[11]

Decorrelated Jet Substructure Tagging using Adversarial Neural Networks

et al.

- Phys.Rev.D 96 (2017) 7, 074034
•
e-Print:
- 1703.03507
•
DOI:
- 10.1103/PhysRevD.96.074034

[12]

Machine Learning Uncertainties with Adversarial Neural Networks

Christoph Englert
(
- SUPA, UK
)
,
Peter Galler
(
- SUPA, UK
)
,
Philip Harris
(
- MIT, Cambridge, Dept. Phys.
)
,
Michael Spannowsky
(
- Durham U., IPPP
)

- Eur.Phys.J.C 79 (2019) 1, 4
•
e-Print:
- 1807.08763
•
DOI:
- 10.1140/epjc/s10052-018-6511-8

[13]

Adversarial domain adaptation to reduce sample bias of a high energy physics event classifier $^{*}$

Jose M. Clavijo
(
- DESY
)
,
Paul Glaysher
(
- DESY
)
,
Jenia Jitsev
(
- IAS, Julich and
- Wuppertal U. and
- Eotvos U.
)
,
Judith M. Katzy
(
- DESY
)

- Mach.Learn.Sci.Tech. 3 (2022) 1, 015014
•
e-Print:
- 2005.00568
•
DOI:
- 10.1088/2632-2153/ac3dde

[14]

Decorrelation with conditional normalizing flows

Samuel Klein
(
- Geneva U.
)
,
Tobias Golling
(
- Geneva U.
)

e-Print:
- 2211.02486

[15]

Decorrelation using optimal transport

- Eur.Phys.J.C 84 (2024) 6, 579
•
e-Print:
- 2307.05187
•
DOI:
- 10.1140/epjc/s10052-024-12868-6

[16]

Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure

James Dolen
(
- SUNY, Buffalo
)
,
Philip Harris
(
- CERN
)
,
Simone Marzani
(
- SUNY, Buffalo
)
,
Salvatore Rappoccio
(
- SUNY, Buffalo
)
,
Nhan Tran
(
- Fermilab
)

- JHEP 05 (2016) 156
•
e-Print:
- 1603.00027
•
DOI:
- 10.1007/JHEP05(2016)156

[17]

Convolved Substructure: Analytically Decorrelating Jet Substructure Observables

Ian Moult
(
- LBNL, Berkeley and
- UC, Berkeley
)
,
Benjamin Nachman
(
- LBL, Berkeley
)
,
Duff Neill
(
- Los Alamos Natl. Lab., Theor. Div.
)

- JHEP 05 (2018) 002
•
e-Print:
- 1710.06859
•
DOI:
- 10.1007/JHEP05(2018)002

[18]

Preserving physically important variables in optimal event selections: A case study in Higgs physics

- JHEP 07 (2020) 001
•
e-Print:
- 1907.02098
•
DOI:
- 10.1007/JHEP07(2020)001

[19]

Enhancing searches for resonances with machine learning and moment decomposition

Ouail Kitouni
(
- MIT, LNS and
- IAIFI, Cambridge
)
,
Benjamin Nachman
(
- LBL, Berkeley
)
,
Constantin Weisser
(
- MIT, LNS and
- IAIFI, Cambridge
)
,
Mike Williams
(
- MIT, LNS and
- MIT and
- Monash U. and
- IAIFI, Cambridge
)

- JHEP 04 (2021) 070,
- JHEP 21 (2020) 070
•
e-Print:
- 2010.09745
•
DOI:
- 10.1007/JHEP04(2021)070

[20]

Online-compatible unsupervised nonresonant anomaly detection

Vinicius Mikuni
(
- LBL, Berkeley
)
,
Benjamin Nachman
(
- UC, Berkeley and
- LBL, Berkeley
)
,
David Shih
(
- Rutgers U., Piscataway
)

- Phys.Rev.D 105 (2022) 5, 055006
•
e-Print:
- 2111.06417
•
DOI:
- 10.1103/PhysRevD.105.055006

[21]

QBDT, a new boosting decision tree method with systematical uncertainties into training for High Energy Physics

Li-Gang Xia
(
- Warwick U.
)

- Nucl.Instrum.Meth.A 930 (2019) 15-26
•
e-Print:
- 1810.08387
•
DOI:
- 10.1016/j.nima.2019.03.088

[22]

Automating the ABCD method with machine learning

Gregor Kasieczka
(
- Hamburg U.
)
,
Benjamin Nachman
(
- LBL, Berkeley
)
,
Matthew D. Schwartz
(
- Harvard U., Phys. Dept.
)
,
David Shih
(
- LBL, Berkeley and
- Rutgers U., Piscataway and
- UC, Berkeley
)

- Phys.Rev.D 103 (2021) 3, 035021
•
e-Print:
- 2007.14400
•
DOI:
- 10.1103/PhysRevD.103.035021

[23]

Performance of massdecorrelated jet substructure observables for hadronic two-body decay tagging in ATLAS

ATLAS Collaboration

Collaboration

[24]

A deep neural network to search for new long-lived particles decaying to jets

CMS

Collaboration

•

Albert M Sirunyan
(
- Yerevan Phys. Inst.
)

et al.

- Mach.Learn.Sci.Tech. 1 (2020) 035012
•
e-Print:
- 1912.12238
•
DOI:
- 10.1088/2632-2153/ab9023

[25]

Reducing the dependence of the neural network function to systematic uncertainties in the input space

Stefan Wunsch
(
- CERN and
- KIT, Karlsruhe, ETP
)
,
Simon Jörger
,
Roger Wolf
(
- KIT, Karlsruhe, ETP
)
,
Günter Quast
(
- KIT, Karlsruhe, ETP
)

- Comput.Softw.Big Sci. 4 (2020) 1, 5
•
e-Print:
- 1907.11674
•
DOI:
- 10.1007/s41781-020-00037-9

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