2025/06/03 by Pralay Kumar Das, Das, Pralay Kumar, Nayana Majumdar +3
Engineering · Computer Science · #Fault Detection and Control Systems #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.2506.02506
A multi-class convolutional neural network (CNN) model has been developed using Keras deep learning library in Python for image-based classification of 12C Hoyle state decay branches from tracking information, recorded by Saha Active Target Time Projection Chamber, SAT-TPC (currently under development). The nuclear events, produced by the 30 MeV α-particle beam in the SAT-TPC, filled with Ar + CO2 (90:10) gas mixture at atmospheric pressure, have been considered for training and validation of the models. The elastic scattering and Hoyle state sequential and direct decay events in the interaction of α-particle with 40Ar, 12C, 16O nuclei have been generated through Monte-Carlo simulation. The three-dimensional tracks, produced by the scattering and decay products through primary ionization of gaseous medium, have been simulated with Geant4. The primary tracks, distributed on the beam-plane, have been convoluted with electron diffusion, obtained with Magboltz, to produce the final tracking information. The classification performance of the proposed model for different readout segmentation schemes of the SAT-TPC has been discussed.