Speaker
Description
The fundamental nature of dark matter so far eludes direct detection experiments, but it has left its imprint in the cosmic large- and small-scale structure. Extracting this information requires accurate modelling of structure formation for different dark matter theories (e.g., the axion), careful handling of astrophysical uncertainties and consistent observations in independent cosmological probes. I will present a novel dark matter science programme for Rubin LSST combining information from galaxy weak lensing and Milky Way stellar streams. I will present forecasts for the sensitivity of LSST year 1 cosmic shear to axion dark matter, for the first time accounting for non-linear axion structure formation and its interplay with astrophysical feedback, demonstrating how to disentangle between axion and feedback effects to the S_8 cosmological parameter discrepancy. I will further present a new deep learning method to detect the lowest-mass sub-halos to-date by their interaction with stellar streams, demonstrating up to 100 x stronger constraints on halo properties than existing approaches. By combining information from larger- and smaller-scale probes, I will argue that compelling dark matter models like the GUT-scale axion can be systematically tested for the first time.
