Snowmass2021 Cosmic Frontier White Paper: Prospects for obtaining Dark Matter Constraints with DESI

Kavli Affiliate: Risa Wechsler

| First 5 Authors: Monica Valluri, Solene Chabanier, Vid Irsic, Eric Armengaud, Michael Walther

| Summary:

Despite efforts over several decades, direct-detection experiments have not
yet led to the discovery of the dark matter (DM) particle. This has led to
increasing interest in alternatives to the Lambda CDM (LCDM) paradigm and
alternative DM scenarios (including fuzzy DM, warm DM, self-interacting DM,
etc.). In many of these scenarios, DM particles cannot be detected directly and
constraints on their properties can ONLY be arrived at using astrophysical
observations. The Dark Energy Spectroscopic Instrument (DESI) is currently one
of the most powerful instruments for wide-field surveys. The synergy of DESI
with ESA’s Gaia satellite and future observing facilities will yield datasets
of unprecedented size and coverage that will enable constraints on DM over a
wide range of physical and mass scales and across redshifts. DESI will obtain
spectra of the Lyman-alpha forest out to z~5 by detecting about 1 million QSO
spectra that will put constraints on clustering of the low-density
intergalactic gas and DM halos at high redshift. DESI will obtain radial
velocities of 10 million stars in the Milky Way (MW) and Local Group satellites
enabling us to constrain their global DM distributions, as well as the DM
distribution on smaller scales. The paradigm of cosmological structure
formation has been extensively tested with simulations. However, the majority
of simulations to date have focused on collisionless CDM. Simulations with
alternatives to CDM have recently been gaining ground but are still in their
infancy. While there are numerous publicly available large-box and zoom-in
simulations in the LCDM framework, there are no comparable publicly available
WDM, SIDM, FDM simulations. DOE support for a public simulation suite will
enable a more cohesive community effort to compare observations from DESI (and
other surveys) with numerical predictions and will greatly impact DM science.

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