Cosmological Simulations of Milky Way-like Systems

I am developing the first suite of cosmological simulations focused on systems with histories that resemble our own galaxy, the Milky Way. These simulations will include a variety of cosmological initial conditions and dark matter models to provide accurate predictions for the impact of early universe and dark sector physics on tracers of dark matter structure in the Milky Way, including ultra-faint dwarf galaxies and stellar streams.

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Dark matter distributions in five Milky Way-like simulations in a standard cold dark matter cosmology. Each image is centered on the dark matter halo that hosts the Milky Way, and analogs of the Milky Way's largest satellite galaxy, the Large Magellanic Cloud, are visible near the center of each system. Adapted from Buch & Nadler et al. (in prep).

Semi-analytic Predictions for Near-field Cosmology

Extracting cosmological information from observations of the local universe requires theoretical predictions for dark matter structure that are both accurate and efficient. I am expanding the open-source structure formation model GALACTICUS to generate constrained realizations of the Milky Way. These predictions will be thousands of times less expensive than existing methods and calibrated to match the results of cosmological simulations.


Example of an assembly history for a Milky Way-mass system (left panel) and its dark matter substructure (right panel) generated using GALACTICUS. Our approach guarantees that key observational constraints on the Milky Way's evolution, including the recent infall of the Large Magellanic Cloud (red circle), are satisfied. Adapted from Nadler et al. (in prep).

Dark Matter Microphysics from Dwarf Galaxies

Ultra-faint dwarf galaxies near the Milky Way inhabit small dark matter halos that are extremely sensitive to dark matter properties. By combining dwarf galaxy observations from the Dark Energy Survey and Pan-STARRS1 with cosmological simulations, galaxy formation models, and particle theory, I have helped place new limits on dark matter’s mass, interactions with the Standard Model, and formation epoch.

These constraints have been combined with strong gravitational lensing data to set among the most stringent limits on warm dark matter to date. I have also developed new predictions for dark matter structure in the presence of dark matter self-interactions, including in the presence of a Large Magellanic Cloud analog.


Constraints on sterile neutrino warm dark matter (left panel) and dark matter--Standard Model interactions (right panel) derived using the population of ultra-faint dwarf galaxies near the Milky Way. Red and blue regions are excluded by our analysis, which improves upon other astrophysical limits and complements direct detection experiments. Adapted from Nadler & Drlica-Wagner et al. (2021).

The Connection between Faint Galaxies and Dark Matter Halos

Understanding the connection between the faintest galaxies and the dark matter halos they form and reside in is a crucial component of galaxy formation and cosmological theory. I have helped extend empirical models of the galaxy–halo connection into the regime of ultra-faint dwarf galaxies. The resulting framework flexibly models satellite disruption due to central galaxies and the detailed relationship between halo and galaxy properties.

Applying this framework to Dark Energy Survey and Pan-STARRS1 data revealed the impact of the Large Magellanic Cloud on the Milky Way satellite population and the efficiency of galaxy formation in low-mass halos. The model's predictions are also consistent with observations of Milky Way analogs from the SAGA Survey.


The fraction of low-mass halos that host galaxies (left panel) and the stellar mass--halo mass relation (right panel), inferred by combining our galaxy--halo connection model with Milky Way satellite observations. Blue regions are allowed by the data, which are consistent with every halo down to ~100 million solar masses hosting a galaxy. Adapted from Nadler & Wechsler et al. (2020).


In addition to cosmological simulations and semi-analytic modeling, I have studied dark matter in the context of the effective field theory of large-scale structure and idealized hydrodynamic simulations of halo formation.

I have applied machine learning techniques to the dark matter problem to efficiently model hydrodynamic simulations and the galaxy–halo connection, and to generate realizations of dark matter substructure.

I have also collaborated on projects including dwarf galaxy detection using astrometric data, modeling star formation in dwarf galaxies, and structure formation limits on dark matter–proton and electron scattering.

Please see Mentorship & Teaching for information about student projects.