A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution; Computing and Software for Big Science; Vol. 2020, iss. 4

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution; Computing and Software for Big Science; Vol. 2020, iss. 4

Λεπτομέρειες βιβλιογραφικής εγγραφής
Parent link:Computing and Software for Big Science
Vol. 2020, iss. 4.— 2020.— [10, 20 p.]
Συγγραφή απο Οργανισμό/Αρχή: Национальный исследовательский Томский политехнический университет Исследовательская школа физики высокоэнергетических процессов
Άλλοι συγγραφείς: Sirunyan A. M., Tumasyan A. R., Adam W. Wolfgang, Ambrogi F. Federico, Tyurin N. E. Nikolay Evgenjevich, Babaev A. A. Anton Anatoljevich
Περίληψη:Title screen
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 bb¯.
Γλώσσα:Αγγλικά
Έκδοση: 2020
Θέματα:
электронный ресурс
труды учёных ТПУ
CMS
b jets
Higgs boson
jet energy
jet resolution
deep learning
Διαθέσιμο Online:https://doi.org/10.1007/s41781-020-00041-z
Μορφή: Ηλεκτρονική πηγή Κεφάλαιο βιβλίου
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=666334

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