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physics.ins-det2023
High-Dimensional Bayesian Likelihood Normalisation for CRESST's Background Model
G. Angloher, S. Banik, G. Benato +59
Using CaWO crystals as cryogenic calorimeters, the CRESST experiment searches for nuclear recoils caused by the scattering of potential Dark Matter particles. A reliable identi…
physics.ins-det2023★ 9 cited
Observation of a low energy nuclear recoil peak in the neutron calibration data of the CRESST-III Experiment
CRESST Collaboration, G. Angloher, S. Banik +58
New-generation direct searches for low mass dark matter feature detection thresholds at energies well below 100 eV, much lower than the energies of commonly used X-ray calibration…
physics.ins-det2022★ 13 cited
Towards an automated data cleaning with deep learning in CRESST
G. Angloher, S. Banik, D. Bartolot +62
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 caref…