A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution
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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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Date issued
2020-10Department
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 LaboratoryJournal
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)
Version: Final published version
ISSN
2510-2044
2510-2036