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

CMS Collaboration

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


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 languageEnglish
Article number10
JournalComputing and Software for Big Science
Issue number1
Publication statusPublished - Dec 2020
Externally publishedYes


  • CMS
  • Deep learning
  • Higgs boson
  • Jet energy
  • Jet resolution
  • b jets


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