Электронный каталог

Статья

Boyko, I.
A New Algorithm for Optimizing the Parameters of a High-Performance Neural Network : Abstract / I.Boyko, N.Huseynov, V.Kiseeva // Физика элементарных частиц и атомного ядра. Письма. – 2025. – Т. 22, № 5. – P. 976. – URL: https://www1.jinr.ru/Pepan_letters/panl_2025_5/12_Boyko_ann.pdf.

This study explores the potential of using artificial neural networks to identify the rare process pp *> tHbq at the Large Hadron Collider, aiming to improve the separation of the signal and background. A neural network-based mathematical tool was developed to improve signal cleaning. This approach was validated using Monte Carlo simulation of signal and background events. We find that neural networks suggest a promising technique for increasing the significance of the signal, facilitating the detection of the process pp *> tHbq.



Спец.(статьи,препринты) = С 325.1а - Нейронные сети и клеточные автоматы
ОИЯИ = ОИЯИ (JINR)2025