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

Abstract

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 $\mathrm{CaW}{\mathrm{O}}_4$ 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 $(68\pm 16)\%$ of the electromagnetic background in the energy range between $1\mathrm{keV}$ and $40\mathrm{keV}$.

19 pages, 12 figures

References in corpus (8)

• Results from 730 kg days of the CRESST-II Dark Matter Search
• Material screening and selection for XENON100
• ACTIVIA: Calculation of Isotope Production Cross-sections and Yields
• Cosmogenic Activation of Materials Used in Rare Event Search Experiments
• Cosmogenic activation of materials
• Background Measurements in the Gran Sasso Underground Laboratory
• Radiopurity of CaWO${}_4$ Crystals for Direct Dark Matter Search with CRESST and EURECA
• Electron and Gamma Background in CRESST Detectors

Cited by in corpus (12)

• First results from the CRESST-III low-mass dark matter program
• Results on sub-GeV Dark Matter from a 10 eV Threshold CRESST-III Silicon Detector
• RES-NOVA sensitivity to core-collapse and failed core-collapse supernova neutrinos
• Observation of a low energy nuclear recoil peak in the neutron calibration data of the CRESST-III Experiment
• Identification of radiopure tungsten for low background applications
• Water Cherenkov muon veto for the COSINUS experiment: design and simulation optimization
• Background model of Phoswich X-ray detector on board small balloon
• Secular Equilibrium Assessment in a ${\mathrm{CaWO}}_4$ Target Crystal from the Dark Matter Experiment CRESST using Bayesian Likelihood Normalisation
• A likelihood framework for cryogenic scintillating calorimeters used in the CRESST dark matter search
• Online triggers for supernova and pre-supernova neutrino detection with cryogenic detectors
• High-Dimensional Bayesian Likelihood Normalisation for CRESST's Background Model
• State of the Art in Direct Dark Matter Detectors: Technologies, Performance, and Future Directions