Development and Explainability of Models for Machine-Learning-Based Reconstruction of Signals in Particle Detectors
Abstract
Machine learning methods are being introduced at all stages of data reconstruction and analysis in various high-energy physics experiments. We present the development and application of convolutional neural networks with modified autoencoder architecture for the reconstruction of the pulse arrival time and amplitude in individual scintillating crystals in electromagnetic calorimeters and other detectors. The network performance is discussed as well as the application of xAI methods for further investigation of the algorithm and improvement of the output accuracy.
Type
Publication
Particles
Kalina Dimitrova
Principal Investigator/Professor
Venelin Kozhuharov
Affiliated Researcher
