Search Talks

Galaxies are the primary tracers of the large-scale structure of the Universe and are traditionally used through summary statistics such as correlation functions and power spectra to constrain cosmological models. However, galaxy populations themselves contain rich information through their spatial distribution, environments, and internal properties, potentially extending beyond the information captured by standard summary statistics. In this talk, I will present a series of machine learning approaches designed to extract cosmological information directly from galaxy catalogs. I will discuss work using graph neural networks (GNNs) to infer cosmological parameters from simulated galaxy populations, including studies demonstrating robustness to observational effects and domain shifts across semi-analytic and hydrodynamical galaxy formation models. I will also present recent work exploring the cosmological information content of individual galaxies using symbolic regression, as well as ongoing efforts based on probabilistic generative models. Together, these results suggest that galaxy populations may encode cosmological information in ways that complement traditional large-scale structure analyses, opening new avenues for field-level and simulation-based inference in upcoming surveys

We perform for the first time full simulation-based inference on the Lyman-$\alpha$ forest 1D power spectrum. In particular, we consider the prediction of the Lyman-$\alpha$ forest $P_{\rm 1D}(k)$ at $2.0

The future of large-scale structure is at high redshift. In particular, the surveys of the next decade that will be at the vanguard of precision cosmology will target star-forming galaxies at z>2. Field-level inference efforts will, therefore, inevitably turn toward high-redshift samples in the near future, the clustering of which is not yet well-understood. The computational intractability of hydrodynamical simulations means that EFT-bias models at the field level will be especially compelling choices of field-level inference forward models for these samples. I will present my recent work measuring the EFT-bias parameters of simulated high-redshift galaxies at the field-level for the first time. I will also discuss how these results can be leveraged toward building simulation-based priors, which aid current field-level inference efforts.

Current and future high-redshift spectroscopic surveys such as DESI, DESI-II, and Spec-S5 raise the question of how to fully extract the information contained in these datasets. Field-level inference is opening a new frontier in cosmology by enabling analyses that capture the full information content of cosmological observations, rather than relying on two- or three-point summary statistics. High-redshift surveys probe unprecedented cosmological volumes, access quasi-linear modes sensitive to fundamental physics, benefit from precise calibration against small-scale hydrodynamic simulations, and exhibit diminishing shot noise toward higher redshift. However, accurately modeling these observations — particularly at the field level — remains a major challenge. I will present recent progress in modeling the Lyman-alpha forest at the field level and compare the information content of joint power spectrum and compressed bispectrum analyses with field-level inference approaches, combining methods from effective field theory (EFT) and machine learning (ML). This framework is then extended to additional high-redshift tracers, including Lyman-break galaxies and Lyman-alpha emitters, whose cross-correlations unlock new opportunities for extracting cosmological information and controlling systematic uncertainties. Finally, I will present ongoing work that combines perturbative mock generation with generative ML techniques to bridge large and small scales, enabling simulations that span DESI-like cosmological volumes while retaining the small-scale structure captured by hydrodynamic simulations. Together, these developments provide a scalable path toward fully exploiting next-generation surveys such as DESI-II and Spec-S5, establishing high-redshift structure as a key arena for precision, field-level cosmology.