Data-driven calibration of large liquid detectors with unsupervised learning
Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment Elsevier 1091 (2026) 171722
Abstract:
This paper demonstrates a novel method to extract photomultiplier tube (PMT) calibration timing constants in large liquid scintillation detectors 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, thus, 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 9300 PMTs in the SNO+ detector, the method has been shown to reliably extract 3 calibration constants for each of the over 7500 online PMTs using radioactive background events. We believe that this basic approach can be straightforwardly generalized for a wide range of applications.Data-Driven Calibration of Large Liquid Detectors with Unsupervised Learning
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