Data-Driven Calibration of Large Liquid Detectors with Unsupervised Learning

TL;DR

This paper introduces an unsupervised deep learning method to calibrate photomultiplier tubes in large liquid detectors using physics data, simplifying the calibration process and demonstrating its effectiveness on the SNO+ detector.

Contribution

The paper presents a novel unsupervised deep learning approach for PMT calibration that leverages a physical model and large-scale regression, applicable to large liquid scintillation detectors.

Findings

Successfully extracted calibration constants for over 7,500 PMTs

Demonstrated reliable calibration using radioactive background events

Method is generalizable to other applications

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 9,300 PMTs in the SNO+ detector, the method has been shown to reliably extract 3 calibration constants for each of the over 7,500 online PMTs using radioactive background events. We believe that this basic approach can be straightforwardly generalised for a wide range of applications.