A deep neural network for simultaneous estimation of b jet energy and resolution (CROSBI ID 290240)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Sirunyan, Albert M ; ... ; Antunović, Željko ; Brigljević, Vuko ; Ferenček, Dinko ; Giljanović, Duje ; Godinović, Nikola ; Kadija, Krešo ; Kovač, Marko ; Lelas, Damir ; Majumder, Devdatta ; Mesić, Benjamin ; Puljak, Ivica ; Roguljić, Matej ; Starodumov, Andrey ; Đurić, Senka ; Šuša, Tatjana ; Šćulac, Toni ; ... ; Trembath-reichert, Stephen
CMS Collaboration
engleski
A deep neural network for simultaneous estimation of b jet energy and resolution
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 TeV at the CERN LHC. The algorithm is trained on a large simulated sample of 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 $\mathrm{; ; ; b\bar{; ; ; b}; ; ; }; ; ; $.
High energy physics ; Experimental particle physics ; LHC ; CMS ; b jets ; Higgs boson ; Jet energy ; Jet resolution ; Deep learning
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Podaci o izdanju
4 (1)
2020.
10
20
objavljeno
2510-2036
2510-2044
10.1007/s41781-020-00041-z