Электронный каталог

Статья

Papoyan, V.
Gradient-Boosted Decision Tree for Particle Identification Problem at MPD : Abstract / V.Papoyan, A.Aparin, A.Ayriyan, H.Grigorian, A.Korobitsin // Физика элементарных частиц и атомного ядра. Письма. – 2025. – Т. 22, № 3. – C. 519. – URL: http://www1.jinr.ru/Pepan_letters/panl_2025_3/25_Papoyan_ann.pdf.

We present the results of a gradient-boosted decision trees application to the particle identification problem in the MPD experiment at the NICA accelerator complex of the Joint Institute for Nuclear Research. Particle identification is one of the significant tasks in the MPD experiment. Since particle identification data are structured in general and at MPD in particular, gradient boosting algorithms were considered. Four gradient boosting implementations were discussed. Numerical studies were conducted to determine the differences between them. A comparison of accuracy and speed was made using Monte Carlo data. It was generated with minimum bias, bismuth and bismuth (Bi + Bi) collisions at *%s&sub(NN) = 9.2 GeV, which is expected to be the first colliding system at MPD. The results obtained demonstrate what kind of algorithms will be the most suitable according to the computing conditions of a physical experiment.



Спец.(статьи,препринты) = С 343 е2 - Взаимодействие релятивистских ядер с ядрами
Спец.(статьи,препринты) = С 17 к - Расчеты по молекулярной динамике. Численное моделирование физических задач
Спец.(статьи,препринты) = С 17 б - Численное интегрирование. Методы Монте-Карло
ОИЯИ = ОИЯИ (JINR)2025