A Bayesian estimate of the primordial helium abundance

Craig J. Hogan, Keith A. Olive, Sean T. Scully

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18 Scopus citations

Abstract

We introduce a new statistical method to estimate the primordial helium abundance Yp from observed abundances in a sample of galaxies that have experienced stellar helium enrichment. Rather than using linear regression on metal abundance, we construct a likelihood function using a Bayesian prior, where the key assumption is that the true helium abundance must always exceed the primordial value by an amount that may be as large as some maximum enrichment w. We compute the likelihood as a function of the two parameters Yp and w using samples of measurements compiled from the literature. We find estimates of Yp between 0.221 and 0.236, depending on the specific subsample and prior adopted, consistent with previous estimates using different techniques. We detect evidence for stellar enrichment (w≠0) even in the lowest metallicity subsamples, but in all samples the most conservative upper bound on Yp occurs for w = 0. This yields a model-independent bound Yp < 0.243 at 95% confidence, favoring a low cosmic baryon density and a high primordial deuterium abundance. The main uncertainty in the Yp bound is not the model of stellar enrichment but possible common systematic biases in the estimate of Y in each individual H II region.

Original languageEnglish (US)
Pages (from-to)L119-L122
JournalAstrophysical Journal
Volume489
Issue number2 PART II
DOIs
StatePublished - 1997

Bibliographical note

Funding Information:
We would like to thank E. Skillman and G. Steigman for helpful conversations. We are grateful to the Institute for Nuclear Theory, Seattle, funded by DOE, for sponsoring the 1996 Workshop on Nucleosynthesis in the Big Bang, Stars, and Supernovae, where this work was started. This work was also supported at the University of Washington by NASA and NSF, and at the University of Minnesota in part by DOE grant DE-FG02-94ER-40823.

Keywords

  • Cosmology: observations
  • Galaxies: abundances
  • Methods: statistical

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