Patricia Iglesias Navarro

Patricia Iglesias Navarro

she/her

PhD researcher in galaxy formation and evolution at the IAC. Working on Bayesian SED fitting, machine learning and simulation-based inference applied to JWST, Euclid and LSST.

About me

I am a predoctoral researcher in galaxy formation and evolution at the Instituto de Astrofísica de Canarias (IAC), supervised by Prof. Marc Huertas-Company and Prof. Johan H. Knapen. I hold an FPI fellowship from the Spanish State Research Agency.

My research combines machine learning and simulation-based inference (SBI) with photometric and spectroscopic data to study the physical properties of galaxies across cosmic time. I focus on pixel-level Bayesian SED fitting to map stellar populations and star formation histories, with applications including:

  • The outshinning of stellar masses in resolved galaxy observations
  • The characterisation of star-forming clumps in galaxies
  • The search for Population III stars with JWST
  • The analysis of survey data from JWST (JADES), Euclid and LSST

I am an active member of JADES, the Euclid Collaboration, and the LSST/Rubin Science Collaboration.

When not trying to understand the Universe, I’m likely at sea surfing or sailing, running mountain trails, or off to some remote destination.

Education & Research Background

I completed my Bachelor’s Degree in Physics (2017–2021) at the University of La Laguna (ULL), where my final project used deep learning (CNNs) to identify Blue Nugget galaxies in the TNG50 cosmological simulation. My internship at the IAC focused on self-supervised contrastive learning for morphological classification of Euclid galaxies.

During my Master’s Degree in Astrophysics and Cosmology (2021–2023, ULL), I developed a likelihood-free inference pipeline to estimate star formation histories of galaxies from optical absorption spectra — published in A&A (2024).

I started my PhD in Astrophysics at the IAC in November 2023, working on Bayesian SED fitting applied to JWST and Euclid photometry. Using novel data-driven approaches applied to observations from the most advanced space telescopes, my ultimate goal is to infer stellar population properties of observed galaxies, along with their uncertainties, helping to constrain the mechanisms of galaxy formation and evolution.

News

  • Aug 2026 — New paper: A Pixel-by-Pixel Path to Population III Discovery with JWST
  • Jul 2026 — Invited talk at EAS Annual Meeting 2026, Lausanne
  • Jun 2026 — Research visit at the Kavli Institute for Cosmology, Cambridge (host: Dr. Sandro Tacchella)
  • 2025–2027 — LSST Discovery Alliance Data Science Fellow