High-fidelity reproduction of central galaxy joint distributions with Neural Networks

TL;DR

This paper introduces a neural network-based method to predict probability distributions of central galaxy properties from halo features, capturing intrinsic scatter and improving the modeling of galaxy-halo relationships for cosmological analyses.

Contribution

The work presents a novel neural network approach that predicts joint probability distributions of galaxy properties, accounting for stochasticity and enabling detailed galaxy population modeling.

Findings

Accurately reproduces the intrinsic scatter in galaxy-halo relations.

Matches clustering statistics of galaxy populations with high precision.

Demonstrates the method's effectiveness in modeling galaxy distributions and clustering.

Abstract

The relationship between galaxies and haloes is central to the description of galaxy formation, and a fundamental step towards extracting precise cosmological information from galaxy maps. However, this connection involves several complex processes that are interconnected. Machine Learning methods are flexible tools that can learn complex correlations between a large number of features, but are traditionally designed as deterministic estimators. In this work, we use the IllustrisTNG300-1 simulation and apply neural networks in a binning classification scheme to predict probability distributions of central galaxy properties, namely stellar mass, colour, specific star formation rate, and radius, using as input features the halo mass, concentration, spin, age, and the overdensity on a scale of 3 $h^{-1}$ Mpc. The model captures the intrinsic scatter in the relation between halo and galaxy properties, and can thus be used to quantify the uncertainties related to the stochasticity of the galaxy properties with respect to the halo properties. In particular, with our proposed method, one can define and accurately reproduce the properties of the different galaxy populations in great detail. We demonstrate the power of this tool by directly comparing traditional single-point estimators and the predicted joint probability distributions, and also by computing the power spectrum of a large number of tracers defined on the basis of the predicted colour-stellar mass diagram. We show that the neural networks reproduce clustering statistics of the individual galaxy populations with excellent precision and accuracy.