Speaker
Description
The astrophysics of the first galaxies is undergoing a data revolution, driven by the infrared capabilities of JWST and the coming arrival of Roman, SKA, and other facilities. Yet inferring the physical processes governing the formation and evolution of the earliest galaxies remains difficult: these objects are distant and intrinsically faint, and several well-established scaling relations appear to break down at the highest redshifts, demanding revisions to our current models. This motivates a different approach to extracting knowledge from data — simulation-based inference (SBI), which forward-models the full observational process and yields posteriors without requiring an explicit, tractable likelihood.
We apply this approach to two problems in high-z galaxy formation. First, we build an SBI framework that forward-models entire JWST pointings around bright z > 10 galaxies, using a neural ratio estimator to produce fast, full posteriors on population parameters — luminosity boost versus increased stochasticity in star formation — that standard tools like the UV luminosity function cannot disentangle. Because the model is physically motivated, posteriors from multiple independent pointings combine naturally, and other observables such as the UVLF can be folded directly into the likelihood. Applied to real JWST data, this yields joint constraints favoring a picture of increased stochasticity in early star formation.
Second, we extend the same SBI framework to infer the size and shape of ionized bubbles around z > 6 Lyman-α emitters directly from JWST spectra, replacing likelihood-map approaches with full posteriors and properly modeled non-Gaussian covariances between galaxies.
Together, these applications demonstrate SBI as a general, scalable route to extracting astrophysical information from JWST's high-redshift galaxy environments.