2023/10/04 by Sirui Wu, N. R. Napolitano, Wu, Sirui +11 · 2 citations
Computer Science · Physics and Astronomy · #Astronomy and Astrophysical Research #Astrophysics of Galaxies (astro-ph.GA) #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Gaussian Processes and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.2310.02816
openalex publication_date 2023/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The galaxy total mass inside the effective radius encode important information on the dark matter and galaxy evolution model. Total "central" masses can be inferred via galaxy dynamics or with gravitational lensing, but these methods have limitations. We propose a novel approach, based on Random Forest, to make predictions on the total and dark matter content of galaxies using simple observables from imaging and spectroscopic surveys. We use catalogs of multi-band photometry, sizes, stellar mass, kinematic "measurements" (features) and dark matter (targets) of simulated galaxies, from Illustris-TNG100 hydrodynamical simulation, to train a Mass Estimate machine Learning Algorithm (Mela). We separate the simulated sample in passive early-type galaxies (ETGs), both "normal" and "dwarf", and active late-type galaxies (LTGs) and show that the mass estimator can accurately predict the galaxy dark masses inside the effective radius in all samples. We finally test the mass estimator against the central mass estimates of a series of low redshift (z≤0.1) datasets, including SPIDER, MaNGA/DynPop and SAMI dwarf galaxies, derived with standard dynamical methods based on Jeans equations. Dynamical masses are reproduced within 0.30 dex (∼2σ), with a limited fraction of outliers and almost no bias. This is independent of the sophistication of the kinematical data collected (fiber vs. 3D spectroscopy) and the dynamical analysis adopted (radial vs. axisymmetric Jeans equations, virial theorem). This makes Mela a powerful alternative to predict the mass of galaxies of massive stage-IV surveys' datasets.