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Simulation for the search of dark matter with CRESST

Simulation for the search of dark matter with CRESST

Jochen Schieck (ORCID: 0000-0002-1058-8093)
  • Grant DOI 10.55776/I5420
  • Funding program Principal Investigator Projects International
  • Status ongoing
  • Start December 1, 2021
  • End November 30, 2025
  • Funding amount € 319,998

DACH: Österreich - Deutschland - Schweiz

Disciplines

Computer Sciences (20%); Physics, Astronomy (80%)

Keywords

    Dark Matter, Simulation, Cryogenic Detectors

Abstract

Over 80% of all matter in the universe is not visible, the so-called dark matter. This dark matter could be explained by introducing a new particle, which interacts weakly only with ordinary matter. However, to observe it, the interaction has to be stronger than the gravitational one. The predictions for the mass of this weakly interacting particle spans over several orders of magnitude. Recently, models with a mass range for dark matter particles between a few MeV and a few GeV have generated much interest. The CRESST experiment, which is currently being carried out at the Gran Sasso laboratory in Italy, is optimized for dark matter by searching for dark matter particles scattering with conventional matter. The energy of the scattering process is converted into lattice excitations of the detector crystal, the so-called phonons, and then read out with a sensor at the transition temperature. With the data collected so far by CRESST, no signal has been observed, however, the exclusion limits for dark matter candidates with a mass of less than 2 GeV are the best limit among the experiments for the direct search for dark matter. In the first funding period, the detection threshold for the recoil energy could be lowered down to 30 eV, an improvement of an order of magnitude compared to previous CRESST detectors and one of the lowest thresholds of all experiments. At recoil energies below 200 eV, however, the event rate rises sharply, and the origin of these energy deposits is not yet understood. Studies to identify these energy deposits are a central part of the project. These studies are based on detailed simulations studies and the analysis of experimental data. In addition to decoding this unknown background, different detector materials for the crystals, e.g., sapphire, are to be used in addition to the previously used detector material, calcium tungstate. These crystals and their properties must also be simulated to make full use of the data.

Research institution(s)
  • Österreichische Akademie der Wissenschaften - 100%
International project participants
  • Federica Petricca, Max Planck-Institut München - Germany
  • Stefan Schönert, Technische Universität München - Germany

Research Output

  • 16 Citations
  • 9 Publications
Publications
  • 2024
    Title Optimal Operation of Cryogenic Calorimeters Through Deep Reinforcement Learning
    DOI 10.1007/s41781-024-00119-y
    Type Journal Article
    Author Angloher G
    Journal Computing and Software for Big Science
    Pages 10
    Link Publication
  • 2022
    Title Towards an automated data cleaning with deep learning in CRESST
    DOI 10.48550/arxiv.2211.00564
    Type Preprint
    Author Angloher G
  • 2022
    Title Secular Equilibrium Assessment in a $\mathrm{CaWO}_4$ Target Crystal from the Dark Matter Experiment CRESST using Bayesian Likelihood Normalisation
    DOI 10.48550/arxiv.2209.00461
    Type Preprint
    Author Angloher G
  • 2022
    Title Results on sub-GeV Dark Matter from a 10 eV Threshold CRESST-III Silicon Detector
    DOI 10.48550/arxiv.2212.12513
    Type Preprint
    Author Cresst Collaboration
  • 2022
    Title Latest observations on the low energy excess in CRESST-III
    DOI 10.48550/arxiv.2207.09375
    Type Preprint
    Author Angloher G
  • 2022
    Title Testing spin-dependent dark matter interactions with lithium aluminate targets in CRESST-III
    DOI 10.48550/arxiv.2207.07640
    Type Preprint
    Author Angloher G
  • 2023
    Title High-Dimensional Bayesian Likelihood Normalisation for CRESST's Background Model
    DOI 10.48550/arxiv.2307.12991
    Type Preprint
    Author Angloher G
  • 2023
    Title Observation of a low energy nuclear recoil peak in the neutron calibration data of the CRESST-III Experiment
    DOI 10.48550/arxiv.2303.15315
    Type Preprint
    Author Cresst Collaboration
  • 2023
    Title Latest observations on the low energy excess in CRESST-III
    DOI 10.21468/scipostphysproc.12.013
    Type Journal Article
    Author Angloher G
    Journal SciPost Physics Proceedings
    Pages 013
    Link Publication

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