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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=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¯.
Fuente: Computing and Software for Big Science volume 4, Article number: 10 (2020)
Editorial: Springer
Fecha de publicación: 01/04/2020
Nº de páginas: 20
Tipo de publicación: Artículo de Revista
DOI: 10.1007/s41781-020-00041-z
ISSN: 2510-2036,2510-2044
Url de la publicación: https://doi.org/10.1007/s41781-020-00041-z
Leer publicación
SIRUNYAN, A. M.
JOSE IBAN CABRILLO BARTOLOME
ALICIA CALDERON TAZON
BARBARA CHAZIN QUERO
JORGE DUARTE CAMPDERROS
MARCOS FERNANDEZ GARCIA
PEDRO JOSE FERNANDEZ MANTECA
ANDREA GARCIA ALONSO
GERVASIO GOMEZ GRAMUGLIO
CELSO MARTINEZ RIVERO
PABLO MARTINEZ RUIZ DEL ARBOL
FRANCISCO MATORRAS WEINIG
JONATAN PIEDRA GOMEZ
CEDRIC GERALD PRIEELS
MARIA TERESA RODRIGO ANORO
ALBERTO RUIZ JIMENO
LORENZO RUSSO
LUCA SCODELLARO
IVAN VILA ALVAREZ
JESUS MANUEL VIZAN GARCIA
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