CosmoGLINT: Cosmological Generative Model for Line Intensity Mapping with Transformer

TL;DR

CosmoGLINT is a Transformer-based generative model that creates realistic galaxy populations from dark matter simulations, aiding large-scale structure studies and future survey analyses.

Contribution

It introduces a novel Transformer-based framework for generating galaxy properties conditioned on dark matter halos, trained on hydrodynamic simulations, and applicable across redshifts.

Findings

Accurately reproduces statistical properties of galaxy distributions.

Efficiently generates multiple galaxy population realizations.

Creates realistic mock galaxy lightcones for survey analysis.

Abstract

Modelling star-forming galaxies is crucial for upcoming observations of large-scale matter and galaxy distributions with galaxy redshift surveys and line intensity mapping (LIM). We introduce CosmoGLINT (Cosmological Generative model for Line INtensity mapping with Transformer), a Transformer-based generative framework designed to create realistic galaxy populations from dark matter (DM)-only simulations. CosmoGLINT auto-regressively generates sequences of galaxy properties -- including star formation rate (SFR), distance to the halo centre, and radial and tangential velocities relative to the halo -- conditioned on halo mass. Trained on the IllustrisTNG hydrodynamic simulation, the model reproduces key statistical properties of the original data, including the voxel intensity distribution and the power spectrum both in real and redshift space. It can efficiently generate a number of different realisations of the designated galaxy populations, enabling the creation of mock LIM/redshift survey catalogues from large halo catalogues produced by fast DM-only simulations. We show that our model trained at multiple redshifts can be applied to DM halo lightcone data to generate a realistic mock galaxy lightcone with incorporating the redshift evolution of the galaxy population. The mock catalogues can be readily used to derive statistical quantities and to develop data analysis pipelines for ongoing and future wide-field surveys.