Yuanyuan Zhang (NOIRLab)

Constraining Cosmology with Simulation-based inference and Optical Galaxy Cluster Abundance

In this talk, I will discuss the robustness of a machine learning based method, called "simulation"-based inference (SBI) in the context of cosmological parameter estimation from galaxy cluster counts and masses in simulated optical datasets. We construct ``simulations'' using analytical models for the galaxy cluster halo mass function (HMF) and for the observed richness (number of observed member galaxies) to train and test the SBI method. We compare the SBI parameter posterior samples to those from a Markov Chain Monte Carlo (MCMC) analysis that uses the same analytical models to construct predictions of the observed data vector. The two methods exhibit comparable performance, with reliable constraints derived for the primary cosmological parameters, (Ωm and σ8), and richness-mass relation parameters. 

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