0598476 - FZÚ 2025 RIV GB eng J - Článek v odborném periodiku
Aad, G. - Aakvaag, E. - Abbott, B. - Chudoba, Jiří - Federičová, Pavla - Hejbal, Jiří - Jačka, Petr - Kepka, Oldřich - Kroll, Jiří - Kupčo, Alexander - Latoňová, Věra - Lokajíček, Miloš - Lysák, Roman - Marčišovský, Michal - Mikeštíková, Marcela - Němeček, Stanislav - Šícho, Petr - Staroba, Pavel - Svatoš, Michal - Taševský, Marek … celkem 2914 autorů
Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network.
Machine Learning-Science and Technology. Roč. 5, č. 3 (2024), č. článku 035051. E-ISSN 2632-2153
Výzkumná infrastruktura: CERN-CZ III - 90240
Institucionální podpora: RVO:68378271
Klíčová slova: ATLAS * detector * CERN jets * calibrations
Obor OECD: Particles and field physics
Impakt faktor: 6.3, rok: 2023
Způsob publikování: Open access

The energy and mass measurements of jets are crucial tasks for the Large Hadron Collider experiments. This paper presents a new calibration method to simultaneously calibrate these quantities for large-radius jets measured with the ATLAS detector using a deep neural network (DNN). To address the specificities of the calibration problem, special loss functions and training procedures are employed, and a complex network architecture, which includes feature annotation and residual connection layers, is used. The DNN-based calibration is compared to the standard numerical approach in an extensive series of tests. The DNN approach is found to perform significantly better in almost all of the tests and over most of the relevant kinematic phase space. In particular, it consistently improves the energy and mass resolutions, with a 30% better energy resolution obtained for transverse momenta pT>500pT​>500 GeV.
Trvalý link: https://hdl.handle.net/11104/0356144