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The galaxy total mass inside the effective radius encode importantinformation on the dark matter and galaxy evolution model. Total "central"masses can be inferred via galaxy dynamics or with gravitational lensing, butthese methods have limitations. We propose a novel approach, based on RandomForest, to make predictions on the total and dark matter content of galaxiesusing simple observables from imaging and spectroscopic surveys. We usecatalogs of multi-band photometry, sizes, stellar mass, kinematic"measurements" (features) and dark matter (targets) of simulated galaxies, fromIllustris-TNG100 hydrodynamical simulation, to train a Mass Estimate machineLearning Algorithm (Mela). We separate the simulated sample in passiveearly-type galaxies (ETGs), both "normal" and "dwarf", and active late-typegalaxies (LTGs) and show that the mass estimator can accurately predict thegalaxy dark masses inside the effective radius in all samples. We finally testthe mass estimator against the central mass estimates of a series of lowredshift (z$\leq$0.1) datasets, including SPIDER, MaNGA/DynPop and SAMI dwarfgalaxies, derived with standard dynamical methods based on Jeans equations.Dynamical masses are reproduced within 0.30 dex ($\sim2\sigma$), with a limitedfraction of outliers and almost no bias. This is independent of thesophistication 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 ofgalaxies of massive stage-IV surveys' datasets.

Total and dark mass from observations of galaxy centers with Machine Learning