{"ID":2841676,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11221","arxiv_id":"2511.11221","title":"Sparse Methods for Vector Embeddings of TPC Data","abstract":"Time Projection Chambers (TPCs) are versatile detectors that reconstruct charged-particle tracks in an ionizing medium, enabling sensitive measurements across a wide range of nuclear physics experiments. We explore sparse convolutional networks for representation learning on TPC data, finding that a sparse ResNet architecture, even with randomly set weights, provides useful structured vector embeddings of events. Pre-training this architecture on a simple physics-motivated binary classification task further improves the embedding quality. Using data from the GAseous Detector with GErmanium Tagging (GADGET) II TPC, a detector optimized for measuring low-energy $β$-delayed particle decays, we represent raw pad-level signals as sparse tensors, train Minkowski Engine ResNet models, and probe the resulting event-level embeddings which reveal rich event structure. As a cross-detector test, we embed data from the Active-Target TPC (AT-TPC) -- a detector designed for nuclear reaction studies in inverse kinematics -- using the same encoder. We find that even an untrained sparse ResNet model provides useful embeddings of AT-TPC data, and we observe improvements when the model is trained on GADGET data. Together, these results highlight the potential of sparse convolutional techniques as a general tool for representation learning in diverse TPC experiments.","short_abstract":"Time Projection Chambers (TPCs) are versatile detectors that reconstruct charged-particle tracks in an ionizing medium, enabling sensitive measurements across a wide range of nuclear physics experiments. We explore sparse convolutional networks for representation learning on TPC data, finding that a sparse ResNet archi...","url_abs":"https://arxiv.org/abs/2511.11221","url_pdf":"https://arxiv.org/pdf/2511.11221v1","authors":"[\"Tyler Wheeler\",\"Michelle P. Kuchera\",\"Raghuram Ramanujan\",\"Ryan Krupp\",\"Chris Wrede\",\"Saiprasad Ravishankar\",\"Connor L. Cross\",\"Hoi Yan Ian Heung\",\"Andrew J. Jones\",\"Benjamin Votaw\"]","published":"2025-11-14T12:25:54Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"nucl-ex\"]","methods":"[]","has_code":false}