Decoding Dark Matter with Stellar Streams from Beyond the Milky Way

Introduction

One of the key questions driving astrophysics research today is the nature of dark matter, which comprises 80% of the matter in the Universe. Stellar streams are sensitive to the distribution of dark matter and to the population of dark matter subhalos in galaxies, both of which depend on the mass and interactions of the dark matter particle.

Proposed Work

My proposed work will use the wealth of incoming stellar stream data materializing over the next five years from the following sources:

• Nancy Grace Roman Space Telescope
• Vera C. Rubin Observatory
• Euclid Space Telescope

The goal is to measure dark matter halo masses, shapes, and concentrations, as well as subhalo populations of external galaxies.

Methodology

I will lead a fundamental shift in the approach to stellar stream studies through statistical model-to-data comparisons between theoretical predictions from various dark matter candidates:

1. Cold
2. Warm
3. Wave-like
4. Self-interacting

These comparisons will be made against the actual stream data.

Development of Techniques

To achieve this goal, I will develop novel numerical techniques which will:

• Model and fit multiple streams at once in multiple external galaxies
• Run state-of-the-art N-body simulations of disrupting globular clusters in dwarf galaxies to place theoretical constraints on the expected substructure
• Carry out statistical comparisons between dark matter models and properties derived from the stellar stream data

Expected Outcomes

I will rule out dark matter candidates that are inconsistent with the new stellar stream data. By the end of the 60-month grant period, I will have the world-leading constraints on dark matter from stellar streams.

Significance

This work provides an innovative method for mapping the otherwise invisible dark matter and will constrain statistical properties of dark matter related to its nature and possible extensions of the standard model of particle physics.