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

CERN
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Research output: Contribution to journal › Article › peer-review

36 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 - Dec 2020
Externally published	Yes

Keywords

CMS
Deep learning
Higgs boson
Jet energy
Jet resolution
b jets

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10.1007/s41781-020-00041-z

Cite this

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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 Chhibra, \{S. S.\} and J. D{\textquoteright}Hondt and \{De Clercq\}, J. and D. Lontkovskyi and S. Lowette and I. Marchesini and S. Moortgat and Q. Python and K. Skovpen and O. Bouhali",

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

year = "2020",

month = dec,

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

language = "English",

volume = "4",

journal = "Computing and Software for Big Science",

issn = "2510-2044",

publisher = "Springer Nature",

number = "1",

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TY - JOUR

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 - Chhibra, S. S.

AU - D’Hondt, J.

AU - De Clercq, J.

AU - Lontkovskyi, D.

AU - Lowette, S.

AU - Marchesini, I.

AU - Moortgat, S.

AU - Python, Q.

AU - Skovpen, K.

AU - Bouhali, O.

PY - 2020/12

Y1 - 2020/12

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

UR - https://www.scopus.com/pages/publications/85097776096

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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