Sparse Methods for Vector Embeddings of TPC Data
This work addresses representation learning for TPC data in nuclear physics experiments, offering a general tool that is incremental as it adapts existing sparse methods to this domain.
The paper tackled the problem of learning vector embeddings from Time Projection Chamber (TPC) data using sparse convolutional networks, finding that a sparse ResNet architecture, even with random weights, provides useful structured embeddings, with pre-training on a binary classification task further improving quality, as demonstrated on GADGET II and AT-TPC datasets.
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.