Mimicking the halo-galaxy connection using machine learning
  Elucidating the connection between the properties of galaxies and the
properties of their hosting haloes is a key element in theories of galaxy
formation. When the spatial distribution of objects is also taken under
consideration, investigating the halo-galaxy connection becomes very relevant
for cosmological measurements. In this paper, we use machine learning (ML)
techniques to analyze these intricate relations in the IllustrisTNG300
magnetohydrodynamical simulation. We employ four different algorithms:
extremely randomized trees (ERT), K-nearest neighbors (kNN), light gradient
boosting machine (LGBM), and neural networks (NN), along with a stacked model
where we combine results from all four approaches. Overall, the different ML
algorithms produce consistent results in terms of predicting galaxy properties
from a set of input halo properties that include halo mass, concentration,
spin, and halo overdensity. For stellar mass, the (predicted v. true) Pearson
correlation coefficient is 0.98, dropping down to 0.7-0.8 for specific star
formation rate (sSFR), colour, and size. In addition, we test an existing data
augmentation technique, designed to alleviate the problem of unbalanced
datasets, and show that it improves slightly the shape of the predicted
distributions. We also demonstrate that our predictions are good enough to
reproduce the power spectra of multiple galaxy populations, defined in terms of
stellar mass, sSFR, colour, and size with high accuracy. Our results align with
previous reports suggesting that certain galaxy properties cannot be reproduced
using halo features alone.
