Suppression of 14C photon hits in large liquid scintillator detectors via spatiotemporal deep learning

Liquid scintillator detectors are widely used in neutrino experiments due to their low energy threshold and high energy resolution. Despite the tiny abundance of 14C in LS, the photons induced by the β decay of the 14C isotope inevitably contaminate the signal, degrading the energy resolution. In this work, we propose three models to tag 14C photon hits in e^+ events with 14C pile-up, thereby suppressing its impact on the energy resolution at the hit level: a gated spatiotemporal graph neural network and two Transformer-based models with scalar and vector charge encoding. For a simulation dataset in which each event contains one 14C and one e^+ with kinetic energy below 5 MeV, the models achieve 14C recall rates of 25%-48% while maintaining e^+ to 14C misidentification below 1%, leading to a large improvement in the resolution of total charge for events where e^+ and 14C photon hits strongly overlap in space and time.