20-24 March 2023
Haus H, Telegrafenberg
Europe/Berlin timezone

DESI and the matter density distribution of the Milky Way from field halo stars

20 Mar 2023, 17:25
Haus H, Telegrafenberg

Haus H, Telegrafenberg

Potsdam, Germany
Contributed talk SESSION 2 : Sizes, Masses, and Formation histories of the Milky Way and of Andromeda SESSION 2 : Sizes, Masses, and Formation histories of the Milky Way and of Andromeda


Monica Valluri (University of Michigan)


The Dark Energy Spectroscopic Instrument (DESI) is currently one of the most powerful instruments for wide-field multi-object spectroscopy. The synergy of DESI with current (e.g. ESA’s Gaia satellite) and future observing facilities including the Vera Rubin Observatory’s Legacy Survey of Space and Time (LSST), and the Nancy Grace Roman Space Telescope’s High Latitude Survey (HLS) will yield datasets of unprecedented size and coverage that will enable strong constraints on the dark matter distribution in the Milky Way (MW) and Local Group galaxies including M31. By the end of 2024 DESI spectra (350nm-980nm) will be obtained for 7.2 million stars in the main program and for up to another 5 million stars via the backup program (bright/poor sky condition) in the MW alone. As of May 2021 (when Kitt Peak shut down due to the Conteras Fire) DESI had already obtain spectra of 3.6 million unique stars. After a brief introduction to the DESI MW survey, its spectroscopic pipeline and what to expect in the early data release (mid 2023), I will present results from two new modeling codes that have been recently developed to constrain the mass distribution of the MW that utilize the full 6D as well as 5D phase space data. First I will describe a new B-spline based non-parametric spherical Jeans modeling code (NIMBLE, Rehemtulla et al. 2022). Tests of NIMBLE with mock data from the Latte cosmological simulations show that it is possible to constraint the mass of the MW out to ~80kpc with ~15\% accuracy even in the presence of halo substructure and moderate amounts of disequilibrium. I will then show preliminary results of the application of NIMBLE to Survey Validation data from DESI. Finally, I will describe results from an axisymmetric distribution function fitting code (Hattori et al. 2021) and results from its application to Gaia RRLyrae data.

Do you plan to attend the symposium in-person or virtually? in-person

Primary author

Monica Valluri (University of Michigan)

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