Photon/electron classification in liquid argon detectors by means of Soft Computing

In the field of Particle Physics, the behaviors of elementary particles differ among themselves on subtle details that need to be identified to further our understanding of the universe. Machine learning is being increasingly applied in order to solve this task by extracting and extrapolating patterns from detector data. This paper tackles the classification of simulated traces from a liquid argon container into photon-or electron -induced events. Several viable dataset representations are proposed and evaluated on nine supervised learning algorithms to find promising combinations. After that, a hyperparameter optimization step is applied on some of the classifiers to try to maximize their accuracy. Random Forest and XGBoost achieve the best results with roughly 88% test-set accuracy, which shows the potential of machine learning to solve a significant research question in a subfield that is expected to keep growing in the coming years.