TY - JOUR

T1 - Detector Optimization Based on Artificial Neural Network Training

AU - Roudnev, V. A.

AU - Galaktionov, K. A.

AU - Valiev, F. F.

PY - 2025/8/1

Y1 - 2025/8/1

N2 - Artificial neural networks were used for event-wise analysis of model data for a microchannel plate detector. Based on this data, the impact parameter and the collision point coordinates for each event were estimated. An analysis based on several Monte-Carlo collision models was performed. Even though the quality of the existing models of events is not sufficient for a reliable, model-independent estimation of the collision parameters, the proposed method of parameter reconstruction allows one to estimate the optimal technical characteristics of the detector.

AB - Artificial neural networks were used for event-wise analysis of model data for a microchannel plate detector. Based on this data, the impact parameter and the collision point coordinates for each event were estimated. An analysis based on several Monte-Carlo collision models was performed. Even though the quality of the existing models of events is not sufficient for a reliable, model-independent estimation of the collision parameters, the proposed method of parameter reconstruction allows one to estimate the optimal technical characteristics of the detector.

KW - detector

KW - impact parameter

KW - machine learning

KW - microchannel plate

KW - neural network

UR - https://www.mendeley.com/catalogue/3b437bea-c44e-31f4-b363-cb02e0d0240d/

U2 - 10.1134/s1062873825712139

DO - 10.1134/s1062873825712139

M3 - Article

VL - 89

SP - 1335

EP - 1342

JO - Bulletin of the Russian Academy of Sciences: Physics

JF - Bulletin of the Russian Academy of Sciences: Physics

SN - 1062-8738

IS - 8

ER -