Naslov
A deep neural network for simultaneous estimation of b jet energy and resolution

Autori
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

Kolaboracija
CMS Collaboration

Izvornik
Computing and software for big science (2510-2036) 4 (2020), 1; 10, 20

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
High energy physics ; Experimental particle physics ; LHC ; CMS ; b jets ; Higgs boson ; Jet energy ; Jet resolution ; Deep learning

Sažetak
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}; ; ; }; ; ; $.

Izvorni jezik
Engleski

Znanstvena područja
Fizika

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