Speaker
Description
CRESST (Cryogenic Rare Event Search with Superconducting Thermometers) is a direct dark matter detection experiment located at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy. It searches for dark matter–nucleus interactions using scintillating cryogenic calorimeters, pushing its energy threshold ever lower to improve sensitivity to low‑mass dark matter. At these thresholds, however, the analysis is complicated by an exponentially rising event rate below 200 eV, referred to as the low‑energy excess, whose origin remains unknown. This unexplained background challenges traditional, parametrized likelihood models of the data. In this talk, we explore the use of normalizing flows to learn the underlying probability density of the CRESST data directly, and discuss how such data‑driven density estimators can provide an alternative likelihood construction and potentially a more flexible framework for setting sensitivity limits.