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arXiv · 2201.05549

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

Matteo Borghesi·Marco Faverzani·Cecilia Ferrari·Elena Ferri·Andrea Giachero·Angelo Nucciotti·Luca Origo
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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.

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Matteo Borghesi, Marco Faverzani, Cecilia Ferrari, Elena Ferri, Andrea Giachero, Angelo Nucciotti, Luca Origo. 2022-01-14. The matrix optimum filter for Low Temperature Detectors dead-time reduction. https://doi.org/10.1140/epjc%2Fs10052-022-10379-w

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