• Media type: E-Article
• Title: A deep neural network for simultaneous estimation of b jet energy and resolution
• Contributor: Sirunyan, Albert M; Tumasyan, Armen; Jeitler, Manfred; Melo Da Costa, Eliza; Kim, Junho; Kim, Jae Sung; Lee, Haneol; Lee, Kyeongpil; Lee, Sangeun; Nam, Kyungwook; Oh, Minseok; Oh, Sung Bin; Radburn-Smith, Benjamin Charles; Yang, Unki; Da Silveira, Gustavo Gil; Yoo, Hwi Dong; Yoon, Inseok; Jeon, Dajeong; Kim, Ji Hyun; Lee, Jason Sang Hun; Park, Inkyu; Watson, Ian; Choi, Young-Il; Hwang, Chanwook; [...]
• Published: Springer International Publishing, 2020
• Language: English
• ISSN: 25102044; 25102036
• Keywords: Data processing & computer science ; Datenverarbeitung; Informatik
• Description: 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 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 $\hbox {b}\bar{\hbox {b}}$.