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1.
Phys Rev Lett ; 130(21): 211802, 2023 May 26.
Artigo em Inglês | MEDLINE | ID: mdl-37295094

RESUMO

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. We identify the expected peak structure from the single-γ de-excitation of ^{183}W with 3σ and its origin by neutron capture with 6σ significance. This result demonstrates a new method for precise, in situ, and nonintrusive calibration of low-threshold experiments.


Assuntos
Núcleo Celular , Nêutrons , Califórnio , Método de Monte Carlo
2.
Appl Radiat Isot ; 194: 110670, 2023 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-36696751

RESUMO

CRESST is a leading direct detection sub-GeVc-2 dark matter experiment. During its second phase, cryogenic bolometers were used to detect nuclear recoils off the CaWO4 target crystal nuclei. The previously established electromagnetic background model relies on Secular Equilibrium (SE) assumptions. In this work, a validation of SE is attempted by comparing two likelihood-based normalisation results using a recently developed spectral template normalisation method based on Bayesian likelihood. Albeit we find deviations from SE in some cases we conclude that these deviations are artefacts of the fit and that the assumptions of SE is physically meaningful.

3.
Eur Phys J Plus ; 138(1): 100, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36741916

RESUMO

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. With a data set of over one million labeled records from 68 detectors, recorded between 2013 and 2019 by CRESST, we test the capability of four commonly used neural network architectures to learn the data cleaning task. Our best performing model achieves a balanced accuracy of 0.932 on our test set. We show on an exemplary detector that about half of the wrongly predicted events are in fact wrongly labeled events, and a large share of the remaining ones have a context-dependent ground truth. We furthermore evaluate the recall and selectivity of our classifiers with simulated data. The results confirm that the trained classifiers are well suited for the data cleaning task.

4.
Eur Phys J C Part Fields ; 79(10): 881, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31708682

RESUMO

The CRESST (Cryogenic Rare Event Search with Superconducting Thermometers) dark matter search experiment aims for the detection of dark matter particles via elastic scattering off nuclei in CaWO 4 crystals. To understand the CRESST electromagnetic background due to the bulk contamination in the employed materials, a model based on Monte Carlo simulations was developed using the Geant4 simulation toolkit. The results of the simulation are applied to the TUM40 detector module of CRESST-II phase 2. We are able to explain up to ( 68 ± 16 ) % of the electromagnetic background in the energy range between 1 and 40 keV .

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