Galaxy stellar and total mass estimation using machine learning

TL;DR

This study employs convolutional neural networks and gradient boosting to predict galaxy stellar and total masses, as well as mass-to-light ratios, using simulated galaxy images and kinematic maps, reducing reliance on traditional model assumptions.

Contribution

It introduces a CNN-based machine learning approach to accurately estimate galaxy masses and mass-to-light ratios from observational data, breaking degeneracies inherent in conventional methods.

Findings

CNN models predict $M_*/L$ with 0.04 dex uncertainty.

Luminosity is the main predictor for galaxy masses.

Velocity dispersion significantly influences mass predictions.

Abstract

Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning, which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions for the distributions of stellar and dark matter. In this work, we use a general sample of galaxies from the TNG100 simulation to investigate the ability of multi-branch convolutional neural network (CNN) based machine learning methods to predict the central (i.e., within $1-2$ effective radii) stellar and total masses, and the stellar mass-to-light ratio $M_*/L$. These models take galaxy images and spatially-resolved mean velocity and velocity dispersion maps as inputs. Such CNN-based models can in general break the degeneracy between baryonic and dark matter in the sense that the model can make reliable predictions on the individual contributions of each component. For example, with $r$-band images and two galaxy kinematic maps as inputs, our model predicting $M_*/L$ has a prediction uncertainty of 0.04 dex. Moreover, to investigate which (global) features significantly contribute to the correct predictions of the properties above, we utilize a gradient boosting machine. We find that galaxy luminosity dominates the prediction of all masses in the central regions, with stellar velocity dispersion coming next. We also investigate the main contributing features when predicting stellar and dark matter mass fractions ($f_*$, $f_{\rm DM}$) and the dark matter mass $M_{DM}$, and discuss the underlying astrophysics.