Point-cloud based machine learning for classifying rare events in the Active-Target Time Projection Chamber

In this work, we assess the use of machine learning to classify fission events in the Active-Target Time Projection Chamber (AT-TPC) using data from an experiment performed at the National Superconducting Cyclotron Laboratory at Michigan State University. The experiment produces an extremely large quantity of data, less than 3% of which are fission events. Therefore, separating fission events from the background beam events is critical to an efficient analysis. A heuristic method was developed to classify events as Fission or Non-Fission based on hand-tuned parameters. However, this heuristic method places 5% of all events into an Unlabeled category, including 15% of all fission events. We present a PointNet model trained on the data labeled by the heuristic method. This model is then used to generate labels for the events in the Unlabeled category. Using the heuristic and machine learning methods together, we can successfully identify 99% of fission events.