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| Funder | Science and Technology Facilities Council |
|---|---|
| Recipient Organization | University of Oxford |
| Country | United Kingdom |
| Start Date | Sep 30, 2024 |
| End Date | Mar 30, 2028 |
| Duration | 1,277 days |
| Number of Grantees | 2 |
| Roles | Student; Supervisor |
| Data Source | UKRI Gateway to Research |
| Grant ID | 2927708 |
Dark matter makes up ~85% of the matter in the universe, and yet we don't know what it is. The DarkSide-20k experiment aims to discover the nature of dark matter with a broad search strategy. Via interactions with nuclei, DarkSide-20k will search for weakly interacting massive particle dark matter candidates, with masses ranging from 100 MeV up to the Planck scale, high above the energies we can produce at colliders.
Via interactions with electrons, DarkSide-20k can discover keV to GeV-mass hidden sector and warm dark matter candidates. Recent results from the predecessor DarkSide-50 experiment set the most stringent constraints on light dark matter interactions with nuclear and electronic final states (Phys.Rev.Lett. 130 (2023) 10, 101001 [https://inspirehep.net/literature/2122440] and Phys.Rev.Lett. 130 (2023) 10, 101002 [https://arxiv.org/abs/2207.11968] respectively).
Beyond dark matter, DarkSide-20k will measure astrophysical neutrinos with high statistics, making important contributions to understanding fusion in stars and supernovae (JCAP 03 (2021) 043 [https://inspirehep.net/literature/1830505]).
DarkSide-20k is under construction at the Laboratori Nazionali del Gran Sasso (https://www.lngs.infn.it/en) in Italy now, by an international collaboration with >450 members in 14 countries. The Oxford group is a key part of producing the UK's contribution to the photon detection system - 7 m^2 of novel, silicon photomultiplier array detectors that view the dark matter interaction target.
Students joining the Oxford DarkSide group in 2024 will participate in installation and commissioning of the detector onsite, and first dark matter search data analysis. Students will develop expertise in both data analysis and statistical techniques for parameter estimation, as well as cutting-edge hardware development and deployment.
University of Oxford
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