Search for: All records

Modeling detector response is a key challenge in time projection chambers. We cast this problem as an unpaired point cloud translation task, between data collected from simulations and from experimental runs. Effective translation can assist with both noise rejection and the construction of high-fidelity simulators. Building on recent work in diffusion probabilistic models, we present a novel framework for performing this mapping. We demonstrate the success of our approach in both synthetic domains and in data sourced from 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.