Speaker
Description
Event reconstruction in large liquid scintillator neutrino detectors, such as SNO+, rely on hit times from large numbers of photomultiplier tubes (PMTs). We demonstrate a novel method to extract PMT calibration timing constants from physics data using the machinery of unsupervised deep learning. The approach uses a simplified physical model of optical photon transport in the loss function with PMT calibration constants treated as free parameters and the simple assumption that individual events represent point-like emission. The problem is effectively reduced to that of regression on a very large scale made tractable by deep learning architectures and automatic differentiation frameworks. Using data from the SNO+ detector, the method has been shown to reliably extract 4 calibration constants for each PMT using radioactive background events. We believe that this basic approach can be straightforwardly generalised for a wide range of applications.