Light Dark Matter Constraints from SuperCDMS HVeV Detectors Operated Underground with an Anticoincidence Event Selection
SuperCDMS Collaboration: M.F. Albakry, I. Alkhatib, D. Alonso-Gonz\'alez, D.W.P. Amaral, J. Anczarski, T. Aralis, T. Aramaki, I.J. Arnquist, I. Ataee Langroudy, E. Azadbakht, C. Bathurst, R. Bhattacharyya, A.J. Biffl, P.L. Brink, M. Buchanan, R. Bunker, B. Cabrera, R. Calkins

TL;DR
This study reports new constraints on light dark matter interactions using underground SuperCDMS HVeV detectors, achieving significant sensitivity improvements through enhanced shielding, event selection, and data analysis techniques.
Contribution
First underground dark matter-electron interaction constraints using SuperCDMS HVeV detectors with improved sensitivity and novel anticoincidence event selection methods.
Findings
Set upper limits on dark-matter-electron scattering cross section for 0.5-1000 MeV/$c^2$
Constrained dark photon kinetic mixing and axion-like particles in specific mass ranges
Achieved up to 25-fold sensitivity improvement over previous searches
Abstract
This article presents constraints on dark-matter-electron interactions obtained from the first underground data-taking campaign with multiple SuperCDMS HVeV detectors operated in the same housing. An exposure of 7.63 g-days is used to set upper limits on the dark-matter-electron scattering cross section for dark matter masses between 0.5 and 1000 MeV/, as well as upper limits on dark photon kinetic mixing and axion-like particle axioelectric coupling for masses between 1.2 and 23.3 eV/. Compared to an earlier HVeV search, sensitivity was improved as a result of an increased overburden of 225 meters of water equivalent, an anticoincidence event selection, and better pile-up rejection. In the case of dark-matter-electron scattering via a heavy mediator, an improvement by up to a factor of 25 in cross-section sensitivity was achieved.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
