Algorithms for Identification of Nearly-Coincident Events in Calorimetric Sensors

TL;DR

This paper introduces a novel SVD-based method for identifying nearly-coincident events in calorimetric sensors, improving detection accuracy amidst pulse-shape variations and enabling larger sensor arrays for neutrino mass measurements.

Contribution

The authors develop a singular value decomposition approach that effectively distinguishes single and piled-up pulses, accounting for shape variations, advancing detector data processing.

Findings

SVD-based method improves pile-up detection accuracy.

Model accounts for pulse shape variation with amplitude, timing, and baseline.

Enables larger sensor arrays and better neutrino mass constraints.

Abstract

For experiments with high arrival rates, reliable identification of nearly-coincident events can be crucial. For calorimetric measurements to directly measure the neutrino mass such as HOLMES, unidentified pulse pile-ups are expected to be a leading source of experimental error. Although Wiener filtering can be used to recognize pile-up, it suffers errors due to pulse-shape variation from detector nonlinearity, readout dependence on sub-sample arrival times, and stability issues from the ill-posed deconvolution problem of recovering Dirac delta-functions from smooth data. Due to these factors, we have developed a processing method that exploits singular value decomposition to (1) separate single-pulse records from piled-up records in training data and (2) construct a model of single-pulse records that accounts for varying pulse shape with amplitude, arrival time, and baseline level, suitable for detecting nearly-coincident events. We show that the resulting processing advances can reduce the required performance specifications of the detectors and readout system or, equivalently, enable larger sensor arrays and better constraints on the neutrino mass.