Speaker
Description
Photomultiplier tubes (PMTs) are widely deployed at neutrino experiments for photon counting. When multiple photons hit a PMT consecutively, their photoelectron (PE) pulses pile up, hindering precise counting and timing measurements. We introduce Fast Stochastic Matching Pursuit (FSMP) to analyze PMT signal waveforms into individual PEs using a reversible-jump Markov-chain Monte Carlo strategy. We demonstrate that FSMP improves the energy and time resolution of PMT-based experiments and accelerates GPU-based computations. It is suitable for dynode PMTs and can be extended to microchannel-plate (MCP) PMTs. In our laboratory characterization of 8-inch MCP-PMTs, FSMP improves the energy resolution by up to 10% compared to the conventional waveform-integration method.