A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution
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41781_2020_Article_41.pdf
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Author(s)
Abercrombie, Daniel Robert
Allen, Benjamin E.
Baty, Austin Alan
Bi, Ran
Brandt, Stephanie Akemi
Busza, Wit
Cali, Ivan Amos
D'Alfonso, Mariarosaria
Gomez-Ceballos, Guillelmo
Goncharov, Maxim
Date Issued
October 2020
Journal
Computing and Software for Big Science
Publisher
Springer International Publishing
Citation
Sirunyan, A. M. et al. "A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution." Computing and Software for Big Science 4, 10 (October 2020): doi.org/10.1007/s41781-020-00041-z. © 2020 The Author(s)
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Final published version
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 = 13 TeV 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[overline b].
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MIT Department
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Massachusetts Institute of Technology. Laboratory for Nuclear Science
Lincoln Laboratory
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DOI of Published Version
https://doi.org/10.1007/s41781-020-00041-z
