A deep neural network for simultaneous estimation of b jet energy and resolution
(2020) Computing and Software for Big Science — Vol. 4 (2020)
Files
101007_s41781-020-00041-z.pdf
Open Access - Adobe PDF
- 1.57 MB
Details
- Authors
- 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} $ at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data marc:recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 $\,\text {fb}^{-1}$. 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 $\hbox {b}\bar{\hbox {b}}$.
- Affiliations
Citations
Bondu, O., Bruno, G., Caputo, C., David, P., Delaere, C., Delcourt, M., Giammanco, A., Lemaitre, V., Prisciandaro, J., Saggio, A., Vidal Marono, M., Vischia, P., Zobec, J., & et al. (2020). A deep neural network for simultaneous estimation of b jet energy and resolution. Computing and Software for Big Science, 4. https://doi.org/10.1007/s41781-020-00041-z (Original work published 2020)