2026 workshop program
Learning the Cosmic Web: Inferring Cosmic Web Environments of Galaxies from Surveys
The work
Abstract
Forthcoming and ongoing large galaxy surveys such as DESI offer unprecedented opportunities to study how the Cosmic Web influences galaxy formation and evolution.
A central challenge is the inference of galaxy environments in observational data, where the underlying dark matter density is not directly accessible. We present a novel graph neural network framework that learns the mapping from graph-encoded local galaxy connectivity to the large-scale tidal field without explicit density reconstruction. Our method is trained on galaxy catalogues from the IllustrisTNG-300 simulation, with targets computed from the dark-matter tidal tensor. In our initial work (Kololgi et al. 2025, RASTI. DOI: 10.1093/rasti/rzag025), we showed that this approach accurately recovers Cosmic Web structures in simulation data. Building on this, we extend the framework to full simulation-based inference via neural posterior estimation (using normalising flows), and forward-model key observational effects, such as survey selection and redshift-space distortions, yielding calibrated per-galaxy posteriors over the tidal eigenvalues. We are additionally exploring domain adaptation (e.g. CORAL) so that our models better generalise from simulations to observational data.
Our work provides a scalable and physically motivated pathway for probabilistic inference of Cosmic Web environments in galaxy surveys, which will facilitate systematic studies of how large-scale structure shapes galaxy formation and evolution.