Tootfinder

Classification of Hoyle State Decay Branches in Active Target Time Projection Chamber using Neural Network
Pralay Kumar Das, Nayana Majumdar, Supratik Mukhopadhyay
https://arxiv.org/abs/2506.02506

Classification of Hoyle State Decay Branches in Active Target Time Projection Chamber using Neural Network
A multi-class convolutional neural network (CNN) model has been developed using Keras deep learning library in Python for image-based classification of $^{12}$C 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 + CO$_2$ (90:10) gas mixture at atmospheric pressure, have been considered for training and vali…