Patricia Iglesias Navarro

Selected publications:

Simulation-based inference of galaxy properties from JWST pixels

The spectral energy distributions (SEDs) of galaxies offer detailed insights into their stellar populations, capturing key physical properties such as stellar mass, star formation history (SFH), metallicity, and dust attenuation. However, inferring these properties from SEDs is a highly degenerate inverse problem, particularly when using integrated observations and a limited number of photometric bands. We present an efficient Bayesian SED-fitting framework tailored to multiwavelength pixel photometry from the JWST Advanced Deep Extragalactic Survey (JADES). Our method employs simulation-based inference to enable rapid posterior sampling across galaxy pixels, leveraging the unprecedented spatial resolution, wavelength coverage, and depth provided by the survey. It is trained on synthetic photometry generated from MILES stellar population models, incorporating both parametric and non-parametric SFHs, realistic noise, and JADES-like filter sensitivity thresholds. We validate this amortised inference approach on mock datasets, achieving robust and well-calibrated posterior distributions, with an R^2 score of 0.99 for stellar mass. Applying our pipeline to real observations, we derive spatially resolved maps of stellar population properties down to S/N_pixel=5 (averaged over F277W, F356W, F444W) for 1083 JADES galaxies and ~2 million pixels with spectroscopic redshifts. These maps enable the identification of dusty or starburst regions and offer insights into mass growth and the structural assembly. We assess the outshining phenomenon by comparing pixel-based and integrated stellar mass estimates, finding limited impact only in low-mass galaxies (<1e8 Msun) but systematic differences of ~0.20 dex linked to SFH priors. With an average posterior sampling speed of 1e-4 seconds per pixel and a total inference time of ~1 CPU-day for the full dataset, our model offers a scalable solution for extracting high-fidelity stellar population properties from HST+JWST datasets, opening the way for statistical studies at sub-galactic scales.

Submitted to A&A

Deriving the star formation histories of galaxies from spectra with simulation-based inference

Iglesias-Navarro, Patricia ; Huertas-Company, Marc ; Martín-Navarro, Ignacio ; Knapen, Johan H. ; Pernet, Emilie

High-resolution galaxy spectra encode information about the stellar populations within galaxies. The properties of the stars, such as their ages, masses, and metallicities, provide insights into the underlying physical processes that drive the growth and transformation of galaxies over cosmic time. We explore a simulation-based inference (SBI) workflow to infer from optical absorption spectra the posterior distributions of metallicities and the star formation histories (SFHs) of galaxies (i.e. the star formation rate as a function of time). We generated a dataset of synthetic spectra to train and test our model using the spectroscopic predictions of the MILES stellar population library and non-parametric SFHs. We reliably estimate the mass assembly of an integrated stellar population with well-calibrated uncertainties. Specifically, we reach a score of 0.97 R2 for the time at which a given galaxy from the test set formed 50% of its stellar mass, obtaining samples of the posteriors in only 10‑4 s. We then applied the pipeline to real observations of massive elliptical galaxies, recovering the well-known relationship between the age and the velocity dispersion, and show that the most massive galaxies (σ ∼ 300 km s‑1) built up to 90% of their total stellar masses within 1 Gyr of the Big Bang. The inferred properties also agree with the state-of-the-art inversion codes, but the inference is performed up to five orders of magnitude faster. This SBI approach coupled with machine learning and applied to full spectral fitting makes it possible to address large numbers of galaxies while performing a thick sampling of the posteriors. It will allow both the deterministic trends and the inherent uncertainties of the highly degenerated inversion problem to be estimated for large and complex upcoming spectroscopic surveys, such as DESI, WEAVE, or 4MOST.

Iglesias-Navarro et. al (2024)
Find the full list of publications here!