My research broadly aims to uncover what we can learn about the dark matter in our own backyard. Whether that’s quite literally in your backyard on Earth (and perhaps directly detectable) or in the Local Group of nearby galaxies, I study how it is distributed, how it got here, and how confidently we can trust the answers we get to these questions.

Publications

In the feed below, you can find a summary post for each paper I’ve written. See the posts themselves for more details, or view my full publication record on the following databases:

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Dark Matter Velocity Distributions for Direct Detection: Astrophysical Uncertainties are Smaller Than They Appear

May 2025 • Papers

Predictions for the local dark matter speed are crucial for interpreting the data from direct detection experiments, but the current state of the art fails to properly quantify astrophysical uncertainties. I address this shortcoming using the largest, highest-resolution simulation to date, definitively characterizing halo-to-halo variance. I propose a novel analysis procedure that dramatically improves the precision of our prediction compared to previous studies, on par with other systematics.

Cosmological Simulations of Stellar Halos with Gaia Sausage–Enceladus Analogues: Two Sausages, One Bun?

August 2024 • Papers

In this paper, I search Milky Way–like galaxies in IllustrisTNG50 for mergers that resemble the GSE, our most recent major merger, and find them a third of the time. I allow for the GSE to be comprised of two mergers, rather than necessarily being a single merger; these pairs account for approimately a third of the GSEs. It's hard to tell single- from two-merger GSEs apart, except that the single mergers are typically accreted more recently.

Probabilistic Inference of the Structure and Orbit of Milky Way Satellites with Semi-Analytic Modeling

November 2023 • Papers

In this work, I propose a procedure to infer internal and orbital properties of Milky Way satellites using the SatGen semi-analytic model. I provide values for $v_\mathrm{max}$ and $r_\mathrm{max}$ for the classical satellites, varying over astrophysical uncertainties such as the stellar mass–halo mass relation and baryonic feedback perscriptions. I also show inferred values for the central densities, pericenters, probability of group accretion, and more. The method is easily extensible to other properties, and my code is publicly available.