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Abstract:

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.Comment: 12 pages, 8 figures, 6 table

Abstract:

The nature of dark matter and properties of neutrinos are among the most pressing issues in contemporary particle physics. The dual-phase xenon time-projection chamber is the leading technology to cover the available parameter space for weakly interacting massive particles, while featuring extensive sensitivity to many alternative dark matter candidates. These detectors can also study neutrinos through neutrinoless double-beta decay and through a variety of astrophysical sources. A next-generation xenon-based detector will therefore be a true multi-purpose observatory to significantly advance particle physics, nuclear physics, astrophysics, solar physics, and cosmology. This review article presents the science cases for such a detector.

Abstract:

Halide perovskites recently emerged as promising materials for the detection of ionising radiation. Single crystals of halide perovskites exhibit very fast and bright scintillation when cooled and may outperform the best modern scintillators at temperatures below 100 K. In this work we report on low-temperature scintillation properties of CsPbCl₃ single crystals, grown using the Bridgeman method. The temperature dependences of the luminescence and decay kinetics were studied using X-ray excitation. At low temperatures, the crystal exhibits an intense narrow-band emission at about 420 nm with very fast decay kinetics. This emission, of which a characteristic feature is the strong thermal quenching, is attributed to the radiative decays of bound and trapped excitons. The fast, middle, and slow decay time constants obtained from a fit of a sum of exponential functions to the decay curve at 10 K are 0.1, 1 and 11 ns, respectively. The scintillation light yield of CsPbCl₃ at 7 K measured at excitation with α-particles from an ²⁴¹Am source is estimated to be 140 ± 15% of a reference LYSO-Ce crystal and 19000 ± 2000 ph per MeV under 14 keV X-ray excitation at 10 K. It is concluded that owing to a reduced amplitude of the slow decay component, CsPbCl₃ exhibits an ultra-fast scintillation response that is superior to that of other halide perovskites. The combination of sub-nanosecond response time and the encouraging light yield has the potential of establishing this material as first choice for scintillation applications that rely on prompt detector response at cryogenic temperatures.