Speaker
Description
"Liquid argon time projection chambers (LArTPC) detector technology has been at the forefront of some recent (MicroBooNE, ICARUS) and future (Dune) large-scale Neutrino experiments. Due to LArTPC data’s interpretability as a collection of images, it has benefited from advances in ML methods, particularly in the field of computer vision. Yet, how to best apply these methods to 3D LArTPC environments is still an ongoing project. Sonata–a recently developed method of training sparse 3D point clouds using self-supervised learning (SSL)–demonstrates improved performance compared to previous point cloud networks (Wu et al., 2025), and thus could be well-suited for 3D LArTPC data.
Using MicroBooNE LArTPC events (MC baseline and 3 real-data pre-training variants), we applied Low Rank Adaptation fine-tuning to a Sonata-pretrained LArTPC point cloud foundation model. This talk will compare the results of fine-tuning on 2 tasks: particle segmentation and ghost point removal. We hope that this Sonata SSL plus fine-tuning approach of our LArTPC pointcloud can be extended in the future to other detectors with the same technology.
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| Contribution types | Short talk (15min + 5min Q/A) |
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