Classification of Hoyle state decay branches in active target time projection chamber using neural network

A multi-class convolutional neural network model has been developed using the Keras deep learning library in Python for image-based classification of 12 C Hoyle state decay branches from tracking information, to be recorded by a prototype Active Target Time Projection Chamber, SAT-TPC (currently under development), filled with an Ar + CO 2 (90:10) gas mixture at atmospheric pressure. The elastic scattering and Hoyle state sequential and direct decay events, in the interaction of 30 MeV α-particle beam 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 the 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.