Algorithms for Identification of Nearly-Coincident Events in Calorimetric Sensors
arXiv:1512.01608 · doi:10.1007/s10909-015-1402-y
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.
arXiv commentTo appear in J. Low Temperature Physics. 11 pages
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Cited by in corpus (8)
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- Broad-band High-Energy Resolution Hard X-ray Spectroscopy using Transition Edge Sensors at SPring-8
- Working principle and demonstrator of microwave-multiplexing for the HOLMES experiment microcalorimeters
- Status of the HOLMES Experiment to Directly Measure the Neutrino Mass
- Progress in the development of TES microcalorimeter detectors suitable for neutrino mass measurement
- A novel approach for nearly-coincident events rejection
- Data reduction for a calorimetrically measured spectrum of the ECHo-1k experiment