Sirunyan, AM;
Tumasyan, A;
Adam, W;
Ambrogi, F;
Bergauer, T;
Dragicevic, M;
Erö, J;
... CMS Collaboration, .; + view all
(2020)
A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution.
Computing and Software for Big Science
, 4
(1)
, Article 10. 10.1007/s41781-020-00041-z.
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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 b ¯ .
| Type: | Article |
|---|---|
| Title: | A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s41781-020-00041-z |
| Publisher version: | https://doi.org/10.1007/s41781-020-00041-z |
| Language: | English |
| Additional information: | © 2020 Springer Nature Switzerland AG. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | CMS, Deep learning, Higgs boson, Jet energy, Jet resolution, b jets |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10115684 |
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