Speaker
Description
The quenching factor in silicon is a fundamental parameter for particle physics experiments that detect nuclear recoils. This is particularly critical for experiments employing CCD sensors as target materials—such as CONNIE and Atucha—which rely on it to convert ionization signals into nuclear recoil energies. Its importance extends beyond neutrino detection, playing a central role in dark matter searches as well. In this work, we introduce a novel technique for determining the quenching factor using Skipper-CCD technology in combination with an unfolding method. We also explore a machine learning approach for distinguishing between neutron and gamma-induced tracks. The proposed data processing strategy is described, along with an analysis of its limitations and a discussion of the benefits and challenges associated with the two experimental sites.
