# Prediction of galaxy halo masses in SDSS DR7 via a machine learning approach

## TL;DR

This paper introduces a machine learning method to predict galaxy halo masses more accurately than traditional techniques, using SDSS data and synthetic galaxy catalogues, with robust testing across different models.

## Contribution

The study develops and validates a machine learning approach for galaxy halo mass prediction that outperforms existing methods and assesses its robustness across various simulation assumptions.

## Key findings

- ML algorithms outperform HAM and DYN in mass prediction accuracy.
- Training on synthetic data with different assumptions still yields better results than traditional methods.
- The approach successfully applied to SDSS DR7 data to estimate galaxy halo masses.

## Abstract

We present a machine learning (ML) approach for the prediction of galaxies' dark matter halo masses that achieves an improved performance over conventional methods. We train three ML algorithms (\texttt{XGBoost}, Random Forests, and neural network) to predict halo masses using a set of synthetic galaxy catalogues that are built by populating dark matter haloes in N-body simulations with galaxies, and that match both the clustering and the joint-distributions of properties of galaxies in the Sloan Digital Sky Survey (SDSS). We explore the correlation of different galaxy- and group-related properties with halo mass, and extract the set of nine features that contribute the most to the prediction of halo mass. We find that mass predictions from the ML algorithms are more accurate than those from halo abundance matching (\texttt{HAM}) or dynamical mass (\texttt{DYN}) estimates. Since the danger of this approach is that our training data might not accurately represent the real Universe, we explore the effect of testing the model on synthetic catalogues built with different assumptions than the ones used in the training phase. We test a variety of models with different ways of populating dark matter haloes, such as adding velocity bias for satellite galaxies. We determine that, though training and testing on different data can lead to systematic errors in predicted masses, the ML approach still yields substantially better masses than either \texttt{HAM}or \texttt{DYN}. Finally, we apply the trained model to a galaxy and group catalogue from the SDSS DR7 and present the resulting halo masses.