The matrix optimum filter for Low Temperature Detectors dead-time reduction

TL;DR

This paper introduces a matrix optimum filtering method that significantly reduces dead-time in experiments using Low Temperature Detectors, enabling higher event rates without losing data due to overlapping signals.

Contribution

The study presents a novel matrix optimum filtering approach that effectively minimizes dead-time in LTD-based experiments, improving data collection efficiency.

Findings

Dead-time can be substantially reduced using the proposed filtering method.

The matrix optimum filter handles overlapping signals better than traditional methods.

Enhanced data acquisition efficiency in high-rate LTD experiments.

Abstract

Experiments aiming at high sensitivities usually demand for a very high statistics in order to reach more precise measurements. However, for those exploiting Low Temperature Detectors (LTDs), a high source activity may represent a drawback, if the events rate becomes comparable with the detector characteristic temporal response. Indeed, since commonly used optimum filtering approaches can only process LTDs signals well isolated in time, a non-negligible part of the recorded experimental data-set is discarded and hence constitute the dead-time. In the presented study we demonstrate that, thanks to the matrix optimum filtering approach, the dead-time of an experiment exploiting LTDs can be strongly reduced.