Identifying the nature of dark matter using the cosmic web

Cosmic observations emphatically demonstrate that most of the Universe's matter is invisible to light. "Dark matter" (DM) is the scaffold for the Universe, gravitationally attracting ordinary visible matter, and thus is essential to the formation of galaxies, stars, planets and life. Understanding the nature of DM is fundamental to understanding the origin of humanity, but the constituents of DM (particles and interactions) remain a mystery.

This is a critical moment when null results for the traditionally-favoured DM candidate (weakly interacting massive particles) are driving an explosion in well-motivated DM theories that urgently need new discovery strategies. We will imminently map the Universe's large-scale structure with unparalleled fidelity in surveys like Rubin. These surveys will provide a new, powerful testbed for microphysical DM models that current direct detection technology cannot probe, but which cause detectable cosmic signatures — if the theoretical modelling challenge is solved.

As ERF at the pre-eminent astrostatistics group at Imperial College London, I will solve this challenge to detect or exclude the most compelling DM theories by the distinctive gravitational signatures they imprint in the cosmic web of structure. My vision is, thus, to transform how we search for the nature of dark matter by developing precision cosmological structure formation models for novel DM theories, where cosmological observations provide the most powerful — or often only — way to search for DM candidates.

I will definitively detect or exclude wave-like cosmic web effects from ultra-light axion DM (ULDM), whose discovery could point towards a "theory of everything" that seeks to explain all physical phenomena in a single set of laws. I will improve, by a factor of 25, constraints on nuclear/electronic interaction cross sections for light (sub-GeV) particle DM probing novel DM production mechanisms like freeze-in.

I pioneered AI techniques to map accurately cosmic constraints from one DM model to another. Using these methods, I will release public likelihood software that will be used to search for other well-motivated DM scenarios like sterile neutrinos, dark photons or complex dark sectors like atomic dark matter. Combining structure information across 12 billion years, I will test for decaying DM signals.

Since dark matter doesn't observably emit or absorb light, I will instead look for how different DM candidates change the gravitational clumping of visible objects like galaxies and gas in-between galaxies. I will lead DM searches within transformational telescope surveys (Rubin) so that I robustly mitigate astrophysical and instrumental data effects.

I will build on my structure formation modelling expertise that I already used to set world-leading DM limits. To match the coming step-up in data precision, I will build new models of the effect of light, ultra-light and decaying DM on the cosmic web. To scan systematically across DM parameter space, I will combine information from larger-scale (> Mpc) probes like galaxy clustering and their gravitational lensing by DM (Rubin) — with smaller-scale (< Mpc) probes like Lyman-alpha forest absorption and Milky Way stellar stream perturbations (Rubin).

The UK has invested hundreds of millions of pounds in next-generation surveys like Rubin. This programme will maximise their impact by developing theoretical and AI models needed to extract precision DM constraints. My results will set the baseline sensitivity and targets for future DM direct detection experiments like the UK-based AION experiment for ULDM and semiconductor/superconductor-based technology (e.g., TESSERACT) to search for sub-GeV particle DM.