Computational modelling of dark matter interactions
Title: Computational modelling of dark matter interactions
DNr: SNIC 2020/5-440
Project Type: SNIC Medium Compute
Principal Investigator: Riccardo Catena <catena@chalmers.se>
Affiliation: Chalmers tekniska högskola
Duration: 2020-09-29 – 2021-10-01
Classification: 10301
Homepage: https://www.chalmers.se/en/staff/pages/riccardo-catena.aspx
Keywords:

Abstract

This research proposal focuses on one of the most pressing open questions in modern physics: unveiling the nature of dark matter (DM) – our Universe’s invisible and unidentified mass component (cf. European Astroparticle Physics Strategy 2017-2026). Specifically, we plan to address four complementary high-profile research subjects; all of them heavily relying on state-of-the art numerical computations that can only be performed on high performance computing systems, namely: a) Simulating the production of DM particles in the collision between an electron beam and a tungsten target at the Light Dark Matter eXperiment (LDMX) with the MadGraph and MadEvent computer codes. b) Computing the response of germanium and silicon crystals to DM-electron interactions by extending the currently available versions of the QEdark and Quantum ESPRESSO packages. c) Simulating the trajectory of Milky Way DM particles as they cross the Sun and the Earth with our own computer code DaMaSCUS. d) Finally, simulating the interaction between DM particles and the early Universe thermal bath with MadDM – a computer code based on MadGraph architecture. The research contained in this proposal will generate a set of theoretical predictions that will enable us, and the astroparticle physics community, to interpret the result of present and future DM search experiments in terms of general particle physics models. The significance and timeliness of this research proposal is supported by its connection to two prestigious research grants: 1) VR project grant: “Empirical determination of the dark matter particle spin” (R. Catena, PI). 2) KAW project grant: “Light Dark Matter” (R. Catena, co-PI).