A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution
Name
41781_2020_Article_41.pdf
Size
1.57 MB
Format
Adobe PDF
Checksum (MD5)
2977e1ff217c271f369cb7e8be02c31f
Author(s)
Sirunyan, A. M
Tumasyan, A.
Adam, W.
Ambrogi, F.
Bergauer, T.
Dragicevic, M.
Erö, J.
Valle, A. E D
Flechl, M.
Frühwirth, R.
Date Issued
October 30, 2020
Publisher
Springer International Publishing
Citation
Computing and Software for Big Science. 2020 Oct 30;4(1):10
Version
Final published version
Abstract
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
$$\sqrt{s}=13\,\text {TeV} $$
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
$$\,\text {fb}^{-1}$$
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
$$\hbox {b}\bar{\hbox {b}}$$
b
b
¯
.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s41781-020-00041-z
