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. 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.

Secular equilibrium assessment in a CaWO4 target crystal from the dark matter experiment CRESST using Bayesian likelihood normalisation.

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.

Geant4-based electromagnetic background model for the CRESST dark matter experiment.

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 .

[Comparison of free ribonucleotide pool in the spleens of C57BL and DBA/2 mice and in leukemia cells sensitive and resistant to 5-fluorouracil]. / Sravnenie fonda svobodnykh ribonukleotidov v selezenke myshei C57BL i DBA/2 i v kletkakh leikozov, chuvstvitel'nykh i ustoichivykh k 5-ftoruratsilu.

The amount of free purine and pyrimidine ribonucleotides in the spleens of mice (C57Bl and DBA/2) and in lympholeukemia cells (La and L1210), sensitive and with induced resistance to 5-fluorouracil, was determined by chromatography on a column with DEAE-cellulose. It was found that the cytidine ribonucleotide pool in the spleens of DBA/2 mice is 2 times lower as compared to C57Bl mice. The lympholeukemia cells (La and L1210) isolated from the animals also differed in their uridine nucleotide pools. The development of leukemia was accompanied by a decrease in ATP and GTP. No significant changes in the total amount of pyrimidine nucleotides under developing resistance to 5-fluororuacil were observed.