Classification of Hoyle State Decay Branches in Active Target Time Projection Chamber using Neural Network

Abstract

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 $\alpha$-particle beam in the SAT-TPC, filled with Ar + CO$_2$ (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 $\alpha$-particle with $^{40}$Ar, $^{12}$C, $^{16}$O 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.