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Abstract <p>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.</p>

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Detector Optimization Based on Artificial Neural Network Training

  • 期刊论文

  • 发表日期：

    2025年8月18日

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  • V. A. Roudnev,
  • K. A. Galaktionov,
  • F. F. Valiev

摘要

Abstract

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.