Login / Signup

The matrix optimum filter for low temperature detectors dead-time reduction.

Matteo BorghesiMarco FaverzaniCecilia FerrariElena FerriAndrea GiacheroAngelo NucciottiLuca Origo
Published in: The European physical journal. C, Particles and fields (2022)
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.
Keyphrases
  • machine learning
  • computed tomography
  • magnetic resonance
  • big data
  • artificial intelligence