BambooSR: Super-Resolving Dark Matter Detection with Physics-Aware Implicit Neural Field

Dark matter direct-detection experiments reconstruct rare particle interactions from faint optical signals. In the PandaX-4T detector, the transverse position of an interaction is encoded in a secondary-scintillation (S2) light pattern that is sparsely and irregularly sampled by 169 top-array photomultiplier tubes, with inactive channels and incomplete coverage further degrading reconstruction near the detector wall. We introduce BambooSR, which formulates this readout as a continuous-field super-resolution problem. Rather than mapping sensor counts directly to a position, BambooSR reconstructs an event-conditioned light field over the readout plane, guided by physics priors on direct propagation and boundary reflection, and integrates it over finite sensor footprints to produce readouts on a denser virtual layout, including responses at non-operational sensors. To enable supervised training and evaluation, we construct the first signal super-resolution benchmark for a large liquid-xenon time-projection chamber, comprising 112,890 paired low- and high-resolution readouts from photon-level simulation, together with 5,583 real PandaX-4T Run 2 wall events for downstream evaluation. BambooSR outperforms implicit neural representation and geometric baselines, with the largest gains near the detector wall and around the inactive sensor. On real PandaX-4T wall events, BambooSR improves calibrated position-reconstruction resolution by 39.6%. We will release the code and dataset.