Data-Driven Calibration of Large Liquid Detectors with Unsupervised Learning
This paper presents a novel unsupervised deep learning method that utilizes a simplified physical model of optical photon transport to automatically extract photomultiplier tube calibration constants from radioactive background events in the large-scale SNO+ liquid scintillation detector.