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

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36 Citas (Scopus)

Resumen

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 ¯.

Idioma original	Inglés
Número de artículo	10
Publicación	Computing and Software for Big Science
Volumen	4
N.º	1
DOI	https://doi.org/10.1007/s41781-020-00041-z
Estado	Publicada - dic. 2020
Publicado de forma externa	Sí

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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 = "Sirunyan, \{A. M.\} and A. Tumasyan and W. Adam and F. Ambrogi and S. Thakur and \{CMS Collaboration\}",

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

year = "2020",

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

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

AU - Sirunyan, A. M.

AU - Tumasyan, A.

AU - Adam, W.

AU - Ambrogi, F.

AU - Thakur, S.

AU - CMS Collaboration

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

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DO - 10.1007/s41781-020-00041-z

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SN - 2510-2044

VL - 4

JO - Computing and Software for Big Science

JF - Computing and Software for Big Science

IS - 1

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ER -

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

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