Speaker
Description
Moments after the Big Bang, quantum fluctuations in the early Universe were scaled up to cosmological scales by inflation, shaping the initial distribution of matter (Initial Conditions, ICs) into a clumpy (nearly) Gaussian random field at the time of recombination. The first stars and galaxies formed in the densest of these clumps and shone light into the Universe. Their radiation ionized their environs, driving the Universe's transition from a neutral to an ionized state. Modelling these astrophysical processes typically requires the use of semi-analytical models, whose parameters must be tuned using observational tracers. However, these parameters, which describe galaxy properties, are often degenerate with the ICs. As a result, knowing the ICs would enable (1) better constraints on these parameters and (2) a spatial and temporal map of the Universe in the region of interest, serving as a template for future observations.
In this work, we assess the constraining power of an inference of the ICs, using the mock observations of the cosmic 21cm signal from SKA-low as an observational tracer, complementing galaxy surveys.
We use simulation-based inference and, in particular, a Gaussian posterior estimation approach, which is further fine-tuned by a score-based diffusion model.
Our results demonstrate accurate constraints on the ICs (8 million parameters) and showcase their dependency on different observational scenarios.