Machine Learning methods for event classification and vertex reconstruction of the 12C + 12C reaction with the MATE-TPC

摘要: In modern nuclear physics experiments, identifying the events of interest is challenging for nuclear reaction
studies with the active target Time Projection Chamber (TPC). In this work, machine learning techniques are
employed to analyze the complex data of 12C + 12C fusion reaction from a TPC named MATE (multi-purpose
active-target time projection chamber for nuclear experiments). Specifically, we successfully applied Resid-
ual Neural Network (ResNet-50, ResNet-34 and ResNet-18) and Visual Geometry Group (VGG-19) to classify
elastic scattering and fusion reaction events from the 12C + 12C reaction. The classification results of the four
models are nearly identical, with accuracies of approximately 97% for the simulated data and 90% for the exper-
imental data. Moreover, these approaches successfully identify some events that are misclassified by traditional
methods. These models are also applied to classify events from different fusion reaction channels, their classi-
fication accuracies are approximately 95% on the simulated data. In addition, a Convolutional Neural Network
(CNN) model is developed to reconstruct the reaction vertex, providing an alternative strategy for vertex recon-
struction. These results indicate that machine learning techniques can effectively classify the reaction events of
different reaction channels and reconstruct the reaction vertex, thereby paving the way for future analyses of
complex nuclear reaction data.