RESUMO
The usual assumption in direct dark matter searches is to consider only the spin-dependent or spin-independent scattering of dark matter particles. However, especially in models with light dark matter particles O(GeV/c^{2}), operators which carry additional powers of the momentum transfer q^{2} can become dominant. One such model based on asymmetric dark matter has been invoked to overcome discrepancies in helioseismology and an indication was found for a particle with a preferred mass of 3 GeV/c^{2} and a cross section of 10^{-37} cm^{2}. Recent data from the CRESST-II experiment, which uses cryogenic detectors based on CaWO_{4} to search for nuclear recoils induced by dark matter particles, are used to constrain these momentum-dependent models. The low energy threshold of 307 eV for nuclear recoils of the detector used, allows us to rule out the proposed best fit value above.
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.
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.
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 .
RESUMO
Experimental results for the restricted energy loss of pairs created from 1-178 GeV photons in a thin Au target and subsequently passing a CCD detector are presented. It is shown that pairs--when detected close to the creation vertex--suffer a reduced energy loss due to the internal screening of the charges constituting the pair. Furthermore, the ability to measure directly the energy of the pair by calorimetry enables a comparison with theory as a function of energy. The observed phenomenon is in good qualitative agreement with general expectations from the Chudakov effect but indicates a quantitative disagreement with either of two mutually disagreeing theories.