Reconstruction of Dark Matter and Baryon Density From Galaxies: A Comparison of Linear, Halo Model and Machine Learning-Based Methods

Abstract

For many analyses in cosmology it is necessary to reconstruct the likely distribution of unobserved fields, such as dark matter or baryons, from observed luminous tracers. The dominant approach in cosmology has been to use the so-called halo model, which assumes radially symmetric profiles centered around luminous tracers such as galaxies. More recently, field-level machine learning methods have been proposed that can learn to estimate the unobserved field after being trained on simulations. However, it is unclear whether machine learning methods indeed significantly improve over linear methods or the halo model. In this paper we make a systematic comparison of different approaches to reconstruct dark matter and baryons from galaxy data using the CAMELS simulations. We find the best results using a combined GNN-CNN approach. We also provide a general analysis and visualization of the relationship of matter, baryons, halos and galaxies in these simulations to interpret our results.