Calculation of crystal defects induced in CaWO$_{4}$ by 100 eV displacement cascades using a linear Machine Learning interatomic potential

TL;DR

This paper develops a Machine Learning interatomic potential to simulate and analyze crystal defects in CaWO$_{4}$ caused by low-energy nuclear recoils, impacting dark matter and neutrino detection signals.

Contribution

It introduces a novel Machine Learning interatomic potential for molecular dynamics simulations of defect formation in CaWO$_{4}$ at low energies.

Findings

Defects influence energy spectra relevant to dark matter detection

Predicted stored energies vary with recoil energy and type

Neutron capture recoils offer potential for sensitive defect detection

Abstract

We determine the energy stored in the crystal defects induced by $\mathcal{O}(10-100)$\,eV nuclear recoils in low-threshold CaWO$_{4}$ cryogenic detectors. A Machine Learning interatomic potential is developed to perform molecular dynamics simulations. We show that the energy spectra expected from Dark Matter and neutrino coherent scattering are affected by the crystal defects and we provide reference predictions. We discuss the special case of the spectrum of nuclear recoils induced by neutron capture, which could offer a unique sensitivity to the calculated stored energies.