An MCMC determination of the primordial helium abundance

TL;DR

This paper uses Markov Chain Monte Carlo methods to analyze spectroscopic data from metal-poor H II regions, aiming to accurately determine the primordial helium abundance while addressing uncertainties and data quality issues.

Contribution

It introduces an MCMC-based approach for primordial helium abundance estimation, incorporating data quality assessments and bias mitigation techniques.

Findings

Estimated primordial helium abundance Y_p = 0.2534 ± 0.0083

Identified systematic bias introduced by the He I 4026 line absence

Improved data reliability through quality cuts and ^2 analysis

Abstract

Spectroscopic observations of the chemical abundances in metal-poor H II regions provide an independent method for estimating the primordial helium abundance. H II regions are described by several physical parameters such as electron density, electron temperature, and reddening, in addition to y, the ratio of helium to hydrogen. It had been customary to estimate or determine self-consistently these parameters to calculate y. Frequentist analyses of the parameter space have been shown to be successful in these determinations, and Markov Chain Monte Carlo (MCMC) techniques have proven to be very efficient in sampling this parameter space. Nevertheless, accurate determination of the primordial helium abundance from observations of H II regions is constrained by both systematic and statistical uncertainties. In an attempt to better reduce the latter, and better characterize the former, we apply MCMC methods to the large dataset recently compiled by Izotov, Thuan, & Stasinska (2007). To improve the reliability of the determination, a high quality dataset is needed. In pursuit of this, a variety of cuts are explored. The efficacy of the He I 4026 emission line as a constraint on the solutions is first examined, revealing the introduction of systematic bias through its absence. As a clear measure of the quality of the physical solution, a \chi^2 analysis proves instrumental in the selection of data compatible with the theoretical model. In addition, the method also allows us to exclude systems for which parameter estimations are statistical outliers. As a result, the final selected dataset gains in reliability and exhibits improved consistency. Regression to zero metallicity yields Y_p = 0.2534 \pm 0.0083, in broad agreement with the WMAP result. The inclusion of more observations shows promise for further reducing the uncertainty, but more high quality spectra are required.