Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments

TL;DR

This paper evaluates various waveform analysis algorithms for PMT data in neutrino and dark matter experiments, finding that fast stochastic matching pursuit offers superior accuracy and resolution improvements over traditional methods.

Contribution

It introduces and benchmarks the fast stochastic matching pursuit algorithm as the most accurate method for PMT waveform analysis in these experiments.

Findings

Fast stochastic matching pursuit achieves the best accuracy.

Significant improvements in time and energy resolution.

Other advanced methods outperform traditional threshold crossing.

Abstract

Photomultiplier tube (PMT) voltage waveforms are the raw data of many neutrino and dark matter experiments. Waveform analysis is the cornerstone of data processing. We evaluate the performance of all the waveform analysis algorithms known to us and find fast stochastic matching pursuit the best in accuracy. Significant time (up to 2 times) and energy (up to 1.07 times) resolution boosts are attainable with fast stochastic matching pursuit, approaching theoretical limits. Other methods also outperform the traditional threshold crossing approach in time resolution.