Optimal Operation of Cryogenic Calorimeters Through Deep Reinforcement Learning.

Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to sub-GeV/c dark matter interactions with nuclei in current direct detection experiments. In such devices, the temperature of the thermometer and the bias current in its readout circuit need careful optimization to achieve optimal detector performance. This task is not trivial and is typically done manually by an expert.

Erratum: Measurement of the 2νββ Decay Half-Life of ^{130}Te with CUORE [Phys. Rev. Lett. 126, 171801 (2021)].

This corrects the article DOI: 10.1103/PhysRevLett.126.

Observation of a Nuclear Recoil Peak at the 100 eV Scale Induced by Neutron Capture.

Coherent elastic neutrino-nucleus scattering and low-mass dark matter detectors rely crucially on the understanding of their response to nuclear recoils. We report the first observation of a nuclear recoil peak at around 112 eV induced by neutron capture. The measurement was performed with a CaWO_{4} cryogenic detector from the NUCLEUS experiment exposed to a ^{252}Cf source placed in a compact moderator.

Towards an automated data cleaning with deep learning in CRESST.

The CRESST experiment employs cryogenic calorimeters for the sensitive measurement of nuclear recoils induced by dark matter particles. The recorded signals need to undergo a careful cleaning process to avoid wrongly reconstructed recoil energies caused by pile-up and read-out artefacts. We frame this process as a time series classification task and propose to automate it with neural networks.

Characterization of a kg-scale archaeological lead-based PbWO cryogenic detector for the RES-NOVA experiment.

Core-collapse Supernovae (SNe) are one of the most energetic events in the Universe, during which almost all the star's binding energy is released in the form of neutrinos. These particles are direct probes of the processes occurring in the stellar core and provide unique insights into the gravitational collapse. RES-NOVA will revolutionize how we detect neutrinos from astrophysical sources, by deploying the first ton-scale array of cryogenic detectors made from archaeological lead.