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

TL;DR

This paper presents a CNN-based image classification method for identifying Hoyle state decay branches in nuclear physics experiments using data from a specialized active target detector, enhancing decay event analysis.

Contribution

The study introduces a novel CNN model trained on simulated tracking data to classify Hoyle state decay modes in active target TPCs, improving event identification accuracy.

Findings

High classification accuracy achieved on simulated data

Effective handling of different readout segmentation schemes

Potential for real-time decay event analysis

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.