Enviar artigo por via de e-mail Detector optimization based on artificial neural network training
- Autores: Roudnev V.A.1, Galaktionov K.A.1, Valiev F.F.1
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Afiliações:
- St. Petersburg State University
- Edição: Volume 89, Nº 8 (2025)
- Páginas: 1326-1333
- Seção: Fundamental problems and applications of physics of atomic nucleus
- URL: https://journal-vniispk.ru/0367-6765/article/view/356133
- DOI: https://doi.org/10.7868/S3034646025080229
- ID: 356133
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Resumo
We apply artificial neural networks for event-wise analysis of model data for a microchannel plate detector. Based on this data, we estimate an impact parameter, and the collision point coordinates for each event. We have performed the analysis based on several Monte-Carlo collision models. Even though the quality of the existing models is not sufficient for a reliable, model-independent estimation of the collision parameters, the proposed method of parameter reconstruction allows us to estimate the optimal technical characteristics of the detector.
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Sobre autores
V. Roudnev
St. Petersburg State UniversitySt. Petersburg, Russia
K. Galaktionov
St. Petersburg State University
Email: st067889@student.spbu.ru
St. Petersburg, Russia
F. Valiev
St. Petersburg State UniversitySt. Petersburg, Russia
Bibliografia
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