Mapping systematic errors in helium abundance determinations using Markov Chain Monte Carlo
Erik Aver, Keith A. Olive, and Evan D. Skillman

TL;DR
This paper applies Markov Chain Monte Carlo methods to improve the estimation of helium abundance in extragalactic HII regions, addressing biases, degeneracies, and systematic errors in the measurements.
Contribution
It introduces MCMC techniques for helium abundance determination, enhancing exploration of parameter space and reducing biases compared to previous methods.
Findings
MCMC outperforms flux perturbation methods in bias reduction.
Introducing electron temperature priors mitigates false minima.
Systematic errors still limit precision of helium abundance estimates.
Abstract
Monte Carlo techniques have been used to evaluate the statistical and systematic uncertainties in the helium abundances derived from extragalactic H~II regions. The helium abundance is sensitive to several physical parameters associated with the H~II region. In this work, we introduce Markov Chain Monte Carlo (MCMC) methods to efficiently explore the parameter space and determine the helium abundance, the physical parameters, and the uncertainties derived from observations of metal poor nebulae. Experiments with synthetic data show that the MCMC method is superior to previous implementations (based on flux perturbation) in that it is not affected by biases due to non-physical parameter space. The MCMC analysis allows a detailed exploration of degeneracies, and, in particular, a false minimum that occurs at large values of optical depth in the He~I emission lines. We demonstrate that…
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