Learning Optical Map in Liquid Xenon Detector with Poisson Likelihood Loss

Dual-phase liquid xenon time projection chambers (LXeTPC) have been successfully applied in rare event searches in astroparticle physics because of their ability to reach low backgrounds and detect small scintillation signals with photosensors. Accurate modeling of optical properties is essential for reconstructing particle interactions within these detectors as well as for developing data selection criteria. This is commonly achieved with discretized maps derived from Monte Carlo simulation or approximated with empirical analytical models. In this work, we employ a novel approach to this using a neural network trained with a Poisson log-likelihood ratio loss to model the mapping from light source location to the expected light intensity for each photosensor. We demonstrate its effectiveness by integrating it into a likelihood fitter for position reconstruction, simultaneously providing insights into the uncertainty associated with the reconstructed position.