Algorithms for Identification of Nearly-Coincident Events in Calorimetric Sensors

Published

December 29, 2015

Author(s)

Bradley K. Alpert, Elena Ferri, Douglas A. Bennett, Marco Faverzani, Joseph W. Fowler, Andrea Giachero, James P. Hays-Wehle, Angelo Nucciotti, Daniel S. Swetz, Joel N. Ullom

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 comprise 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 problem of obtaining Dirac-deltas 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.

Citation

Journal of Low Temperature Physics

Volume

184

Pub Type

Journals

Download Paper

https://doi.org/10.1007/s10909-015-1402-y

Local Download

Keywords

filter algorithms, high-rate processing, microcalorimeter, uncertainty

Citation

Alpert, B. , Ferri, E. , Bennett, D. , Faverzani, M. , Fowler, J. , Giachero, A. , Hays-Wehle, J. , Nucciotti, A. , Swetz, D. and Ullom, J. (2015), Algorithms for Identification of Nearly-Coincident Events in Calorimetric Sensors, Journal of Low Temperature Physics, [online], https://doi.org/10.1007/s10909-015-1402-y (Accessed December 14, 2024)