Электронный каталог

Статья

Didenko, A.
Application of Deep Neural Networks in Top-Higgs Coupling Analysis with the ATLAS Detector / A.Didenko, I.Yeletskikh // Физика элементарных частиц и атомного ядра. Письма. – 2026. – Т. 23, № 5. – P. 485. – URL: https://www1.jinr.ru/Pepan_letters/panl_2026_5/23_Didenko_ann.pdf.

The associated production of a Higgs boson with a single top quark provides direct sensitivity to the relative phase and magnitude of the top-Higgs coupling. Due to the high similarity of the final-state signature with the dominant backgrounds and extremely small number of signal events, machine learning techniques are required to achieve sufficient signal–background separation. Results of Deep Neural Network (DNN) approaches as a cross-check method for event classification problem in the ATLAS analysis are presented.



Спец.(статьи,препринты) = Ц 849 - Искусственный интеллект. Теория и практика
Спец.(статьи,препринты) = С 346.6д1 - Чармоний, боттомоний, топ-кварк. Очарованные частицы и др. резонансы из тяжелых кварков
ОИЯИ = ОИЯИ (JINR)2026