Sparse Methods for Vector Embeddings of TPC Data Article Swipe
T. Wheeler
,
Raghuram Ramanujan
,
Connor L. Cross
,
Hoi Yan Ian Heung
,
Andrew R. Jones
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2511.11221
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2511.11221
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.
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- http://arxiv.org/abs/2511.11221
- https://arxiv.org/pdf/2511.11221
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Sparse Methods for Vector Embeddings of TPC DataWork title
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preprintOpenAlex work type
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2025Year of publication
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2025-11-14Full publication date if available
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T. Wheeler, Raghuram Ramanujan, Connor L. Cross, Hoi Yan Ian Heung, Andrew R. JonesList of authors in order
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https://arxiv.org/abs/2511.11221Publisher landing page
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https://arxiv.org/pdf/2511.11221Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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