Track classification in Active Target Time Projection Chamber using supervised machine learning methods

In low-energy nuclear physics experiments, an Active Target Time Projection Chamber (AT-TPC) can be advantageous for studying nuclear reaction kinematics. The α-cluster decay of 12C is one such reaction requiring careful investigation due to its vital role in producing heavy elements through astrophysical processes. The breakup mechanism of the Hoyle state, a highly α-clustered state at 7.65 MeV in 12C, has long been an important case of study using different experimental techniques to investigate its decaying branch ratio. The direct decay of the Hoyle state into three α-particles and the sequential decay via the ground state into three α- particles can be identified by tracking the α-particles and measuring their energies, which can be accomplished with AT-TPC. In this work, a numerical model using neural networks, for event classification has been developed for identifying the decay modes of Hoyle state into three α- particles from the background scattering events in the active volume. The reaction kinematics of the decay of 12C have been determined using lowenergy non-relativistic scattering of α-particles with 12C. The event tracks in the active gas medium of the AT-TPC have been generated through primary ionization created by the α-particles produced in the aforementioned reaction. This has been carried out using the Geant4 simulation framework. A Convolutional Neural Network (CNN) model has also been developed to identify all possible decay events of 12C and in the lab frame. These events have been further labeled by binary classification model, which have been trained using simulated data. For this purpose, CNN with hidden layers have been implemented using the high-level deep learning library Keras, written in Python. The model has been tested on the events generated using simulation. Thus, it has been possible to precisely classify the Hoyle state α- particles events. This model can also be beneficial as an automated analysis framework for tagging and separating events from experimental data.