A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

CMS Collaboration

doi:10.1007/s41781-020-00041-z

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

CMS Collaboration

European Organization for Nuclear Research
Yerevan Physics Institute
Institut für Hochenergiephysik
Institute for Nuclear Problems of Belarusian State University
University of Antwerp
Vrjie Universiteit Brussel
c/o DESY
University of Turin
Université Libre de Bruxelles
Ghent University
University of Louvain
Centro Brasileiro de Pesquisas Fisicas
Rua São Francisco de Xavier
São Paulo State University
Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences
University of Sofia
Beihang University
Tsinghua University
Institute of High Energy Physics, Chinese Academy of Sciences
State Key Laboratory of Nuclear Physics and Technology
Zhejiang University
Universidad de Los Andes, Colombia
Universidad de Antioquia
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split
Faculty of Science, University of Split
Ruđer Bošković Institute
University of Cyprus
Charles University
Escuela Politecnica Nacional
Universidad San Francisco de Quito
Egyptian Network of High Energy Physics
National Institute of Chemical Physics and Biophysics, Tallinn
University of Helsinki
Helsinki Institute of Physics
LUT University
Université Paris-Saclay
Ip Paris
Université de Strasbourg
Université Paris-Sud
IGFL, Université de Lyon, Université Lyon 1
Georgian Technical University
Tbilisi State University
RWTH Aachen University
Universität Hamburg
Institute of Meteorology and Climate Research
NCSR Demokritos
University of Athens
National Technical University of Athens
University of Ioannina
Eötvös Loránd University
Wigner Research Centre for Physics
Institute of Nuclear Research ATOMKI
University of Debrecen
Eszterhazy Karoly University
Indian Institute of Science
National Institute of Science Education and Research
Panjab University
University of Delhi
Saha Institute of Nuclear Physics
Indian Institute of Technology Madras
Bhabha Atomic Research Centre
Tata Institute of Fundamental Research, Mumbai
Indian Institute of Science Education and Research (IISER)
Institute for Research in Fundamental Sciences (IPM)
University College Dublin
Università degli studi di Bari Aldo Moro
University of Bologna
Università degli Studi di Catania
University of Florence
LNF-INFN
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University of Milano-Bicocca
University of Naples Federico II
University of Padova
University of Pavia
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University of Pisa
University of Rome
University of Trieste
Kyungpook National University
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Agh University of Science and Technology Faculty of Computer Science
National Centre for Nuclear Research
University of Warsaw
Lisboa
Joint Institute for Nuclear Research, Dubna
Petersburg Nuclear Physics Institute (PNPI)
Institute for Nuclear Research of the Russian Academy of Sciences
Institute for Theoretical and Experimental Physics
Moscow Institute of Physics and Technology
National Research Nuclear University MEPhI
P.N. Lebedev Physical Institute of the Russian Academy of Sciences
Moscow State University
CCLRC Rutherford Appleton Laboratory
Novosibirsk State University
Kurchatov Institute
National Research Tomsk Polytechnic University
Tomsk State University
University of Belgrade
Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT)
Universidad Autónoma de Madrid
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CSIC-Univ. Cantabria
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ETH Zurich
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National Central University
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Imperial College London
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Women and Infants Hospital of Rhode Island-Warren Alpert Medical School of Brown University
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Research output: Contribution to journal › Article › peer-review

37 Citations (Scopus)

Abstract

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton–proton collisions at an energy of s=13TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb-1. A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to b b ¯.

Original language	English
Article number	10
Journal	Computing and Software for Big Science
Volume	4
Issue number	1
DOIs	https://doi.org/10.1007/s41781-020-00041-z
Publication status	Published - 1 Dec 2020

Keywords

CMS
Deep learning
Higgs boson
Jet energy
Jet resolution
b jets

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10.1007/s41781-020-00041-z

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title = "A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution",

abstract = "We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton–proton collisions at an energy of s=13TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb-1. A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to b b ¯.",

keywords = "CMS, Deep learning, Higgs boson, Jet energy, Jet resolution, b jets",

author = "\{CMS Collaboration\} and Sirunyan, \{A. M.\} and A. Tumasyan and W. Adam and F. Ambrogi and T. Bergauer and M. Dragicevic and J. Er{\"o} and Valle, \{A. Escalante Del\} and M. Flechl and R. Fr{\"u}hwirth and M. Jeitler and N. Krammer and I. Kr{\"a}tschmer and D. Liko and T. Madlener and I. Mikulec and N. Rad and J. Schieck and R. Sch{\"o}fbeck and M. Spanring and D. Spitzbart and W. Waltenberger and Wulz, \{C. E.\} and M. Zarucki and V. Drugakov and V. Mossolov and Gonzalez, \{J. Suarez\} and Darwish, \{M. R.\} and \{De Wolf\}, \{E. A.\} and Croce, \{D. Di\} and X. Janssen and A. Lelek and M. Pieters and Sfar, \{H. Rejeb\} and Haevermaet, \{H. Van\} and Mechelen, \{P. Van\} and Putte, \{S. Van\} and Remortel, \{N. Van\} and F. Blekman and Bols, \{E. S.\} and F. Beaudette and \{de Cassagnac\}, \{R. Granier\} and M. Nguyen and C. Ochando and P. Paganini and R. Salerno and A. Zabi and A. Zghiche and \{De Wit\}, A. and A. Cappati",

note = "Publisher Copyright: {\textcopyright} 2020, The Author(s).",

year = "2020",

month = dec,

day = "1",

doi = "10.1007/s41781-020-00041-z",

language = "English",

volume = "4",

journal = "Computing and Software for Big Science",

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T1 - A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

AU - CMS Collaboration

AU - Sirunyan, A. M.

AU - Tumasyan, A.

AU - Adam, W.

AU - Ambrogi, F.

AU - Bergauer, T.

AU - Dragicevic, M.

AU - Erö, J.

AU - Valle, A. Escalante Del

AU - Flechl, M.

AU - Frühwirth, R.

AU - Jeitler, M.

AU - Krammer, N.

AU - Krätschmer, I.

AU - Liko, D.

AU - Madlener, T.

AU - Mikulec, I.

AU - Rad, N.

AU - Schieck, J.

AU - Schöfbeck, R.

AU - Spanring, M.

AU - Spitzbart, D.

AU - Waltenberger, W.

AU - Wulz, C. E.

AU - Zarucki, M.

AU - Drugakov, V.

AU - Mossolov, V.

AU - Gonzalez, J. Suarez

AU - Darwish, M. R.

AU - De Wolf, E. A.

AU - Croce, D. Di

AU - Janssen, X.

AU - Lelek, A.

AU - Pieters, M.

AU - Sfar, H. Rejeb

AU - Haevermaet, H. Van

AU - Mechelen, P. Van

AU - Putte, S. Van

AU - Remortel, N. Van

AU - Blekman, F.

AU - Bols, E. S.

AU - Beaudette, F.

AU - de Cassagnac, R. Granier

AU - Nguyen, M.

AU - Ochando, C.

AU - Paganini, P.

AU - Salerno, R.

AU - Zabi, A.

AU - Zghiche, A.

AU - De Wit, A.

AU - Cappati, A.

PY - 2020/12/1

Y1 - 2020/12/1

N2 - We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton–proton collisions at an energy of s=13TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb-1. A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to b b ¯.

AB - We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton–proton collisions at an energy of s=13TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb-1. A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to b b ¯.

KW - CMS

KW - Deep learning

KW - Higgs boson

KW - Jet energy

KW - Jet resolution

KW - b jets

U2 - 10.1007/s41781-020-00041-z

DO - 10.1007/s41781-020-00041-z

M3 - Article

AN - SCOPUS:85097776096

SN - 2510-2044

VL - 4

JO - Computing and Software for Big Science

JF - Computing and Software for Big Science

IS - 1

M1 - 10

ER -

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

Abstract

Keywords

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