Astrophysical and Cosmological Probes of Dark Matter

TL;DR

This paper reviews the current state of astrophysical and cosmological methods for probing dark matter, emphasizing the need for new theoretical frameworks and advanced techniques like machine learning to uncover its fundamental nature.

Contribution

It highlights the upcoming challenges and necessary theoretical developments to fully utilize new observables in understanding dark matter in the next decade.

Findings

Astrophysical and cosmological observations are consistent but incomplete.

New theoretical tools are needed to interpret upcoming data.

Machine learning will play a key role in dark matter research.

Abstract

While astrophysical and cosmological probes provide a remarkably precise and consistent picture of the quantity and general properties of dark matter, its fundamental nature remains one of the most significant open questions in physics. Obtaining a more comprehensive understanding of dark matter within the next decade will require overcoming a number of theoretical challenges: the groundwork for these strides is being laid now, yet much remains to be done. Chief among the upcoming challenges is establishing the theoretical foundation needed to harness the full potential of new observables in the astrophysical and cosmological domains, spanning the early Universe to the inner portions of galaxies and the stars therein. Identifying the nature of dark matter will also entail repurposing and implementing a wide range of theoretical techniques from outside the typical toolkit of astrophysics, ranging from effective field theory to the dramatically evolving world of machine learning and artificial-intelligence-based statistical inference. Through this work, the theory frontier will be at the heart of dark matter discoveries in the upcoming decade.