Two seminars are better than one: Cosmology with Galaxy Cluster Properties using Machine Learning & Total and dark mass from observations of galaxy centers with machine learning

Galaxy clusters, as the universe’s largest gravitationally-bound systems, are one of the keys for cosmological studies. We’ve developed the first Machine Learning (ML) method using their properties to accurately estimate cosmological parameters like Omega_m, sigma_8, Omega_b, and h_0. This method, trained on mock catalogs from Magneticum hydrodynamical simulations, predicts these parameters with uncertainties ranging from 3% to 14%. Our initial tests demonstrate that ML effectively correlates observed galaxy cluster properties with cosmological parameters, suggesting its future utility in multi-wavelength cluster studies from surveys like LSST, CSST, Euclid, Roman, and eROSITA to refine our understanding of cosmology and baryonic feedback.

Dr. Sirui Wu (Sun Yat-sen University)

Abstract:

The galaxy total mass inside the effective radius encode important information on the dark matter and galaxy evolution model. 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 make use  of the TNG100 catalog to train a Mass Estimate machine Learning Algorithm (MELA). We finally test MELA against the central mass estimates of a series of low redshift (z<0.1) datasets, including SPIDER, MaNGA/DynPop and SAMI dwarf galaxies, derived Jeans equations, showing a limited fraction of outliers and almost no bias. This makes Mela a powerful alternative to predict the mass of galaxies of massive stage-IV surveys’ datasets.