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We apply Markov Chain Monte Carlo (MCMC) to the problem of parametric galaxymodeling, estimating posterior distributions of galaxy properties such asellipticity and brightness for more than 100,000 images of galaxies taken fromDC2, a simulated telescope survey resembling the upcoming Rubin ObservatoryLegacy Survey of Space and Time (LSST). We use a physically informed prior andapply selection corrections to the likelihood. The resulting posterior samplesenable rigorous probabilistic inference of galaxy model parameters and theiruncertainties. These posteriors are one key ingredient in a fully probabilisticdescription of galaxy catalogs, which can ultimately enable a refined Bayesianestimate of cosmological parameters. We systematically examine the reliabilityof the posterior mean as a point estimator of galaxy parameters, and of theposterior width as a measure of uncertainty, under some common modelingapproximations. We implement the probabilistic modeling and MCMC inferenceusing the JIF (Joint Image Framework) tool, which we make freely availableonline.

Markov Chain Monte Carlo for Bayesian Parametric Galaxy Modeling in LSST