The Fast Stochastic Matching Pursuit for Neutrino and Dark Matter Experiments

TL;DR

This paper presents FSMP, a novel algorithm using stochastic matching pursuit and MCMC to improve photon detection resolution in PMT-based neutrino and dark matter experiments, with GPU acceleration.

Contribution

The paper introduces FSMP, a new waveform analysis method that enhances energy and time resolution of PMT signals, applicable to various PMT types and accelerated on GPUs.

Findings

Improves energy resolution by up to 10% in MCP-PMTs.

Enhances timing accuracy of photon detection.

Accelerates analysis with GPU implementation.

Abstract

Photomultiplier tubes (PMTs) are widely deployed at neutrino and dark matter experiments for photon counting. When multiple photons hit a PMT consecutively, their photo-electron (PE) pulses pile up to hinder the precise measurements of the count and timings. We introduce Fast Stochastic Matching Pursuit (FSMP) to analyze the PMT signal waveforms into individual PEs with the strategy of reversible-jump Markov-chain Monte Carlo. We demonstrate that FSMP improves the energy and time resolution of PMT-based experiments and gains acceleration on GPUs. It is suitable for dynode PMTs, and is extensible to microchannel-plate (MCP) PMTs. In the condition of our laboratory characterization of 8-inch MCP-PMTs, FSMP improves the energy resolution by up to 10% from the conventional method of waveform integration.