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 = 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 fnal state, such as the observation of Higgs boson
decay to bb̄. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000459295Publication status
publishedExternal links
Journal / series
Computing and Software for Big ScienceVolume
Pages / Article No.
Publisher
SpringerSubject
CMS; b jets; Higgs boson; Jet energy; Jet resolution; Deep learningOrganisational unit
03593 - Dissertori, Günther / Dissertori, Günther
03904 - Wallny, Rainer / Wallny, Rainer
08803 - Grab, Christoph (Tit.Prof.)
09720 - de Cosa, Annapaola / de Cosa, Annapaola
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