Transformers for Point Cloud Completion in the AT-TPC*

In many experiments with the Active Target Time Projection Chamber, there are breaks in reconstructed particle tracks from nuclear reactions. These breaks in the particle tracks can be the result of a variety of reasons: over biased pads, disconnected pads, or a physical hole in the pad plane. Broken tracks reduce the effectiveness of fitting these particle tracks to extract kinematic information. We propose the use of SnowflakeNet, a deep learning architecture designed for point cloud completion, to fill in the breaks in the tracks for more accurate analyses. The transformer-based architecture of SnowflakeNet enables itself to learn from the context from the point cloud as a sequence and use that “knowledge” in completing the tracks. The model is trained on artificially broken simulated ²²Mg + α and simulated ¹⁶O + α reactions and applied to data from two experiments: ¹⁴C + p at Argonne National Laboratory and ²²Mg + α at the Facility for Rare Isotope Beams. Results will be presented from these experiments.