A Link to the Past: Using Markov Chain Monte Carlo Fitting to Constrain Fundamental Parameters of High-Redshift Galaxies
N. Pirzkal, B. Rothberg, Kim K. Nilsson, S. Finkelstein, Anton, Koekemoer, Sangeeta Malhotra, James Rhoads

TL;DR
This paper introduces PiMC^2, a Bayesian MCMC method for fitting spectral energy distributions of high-redshift galaxies, providing robust constraints on their physical properties despite observational challenges.
Contribution
The paper presents a novel Bayesian MCMC approach for SED fitting that effectively constrains galaxy parameters at high redshift, highlighting its advantages and limitations.
Findings
Photometric accuracy below a few percent is needed to constrain metallicity, age, and dust.
Stellar mass can be reliably estimated within a factor of two without ultra-precise photometry.
Adding IRAC data does not always improve parameter constraints.
Abstract
We have a developed a new method for fitting spectral energy distributions (SEDs) to identify and constrain the physical properties of high-redshift (4 < z < 8) galaxies. Our approach uses an implementation of Bayesian based Markov Chain Monte Carlo (PiMC^2) that allows us to compare observations to arbitrarily complex models and to compute 95% credible intervals that provide robust constraints for the model parameters. The work is presented in 2 sections. In the first, we test PiMC^2 using simulated SEDs to not only confirm the recovery of the known inputs but to assess the limitations of the method and identify potential hazards of SED fitting when applied specifically to high redshift (z>4) galaxies. Our tests reveal five critical results: 1) the ability to confidently constrain metallicity, population ages, and Av all require photometric accuracy better than what is currently…
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