Determination of H II Region Metallicity in the Context of Estimating the Primordial Helium Abundance
O. A. Kurichin, P. A. Kislitsyn, A. V. Ivanchik
Abstract
The primordial $${}^{4}$$ He abundance (Y $${}_{p}$$ ) is one of the key characteristics of Primordial Nucleosynthesis processes that occurred in the first minutes after the Big Bang. Its value depends on the baryon/photon ratio $$\eta\equiv n_{b}/n_{\gamma}$$ , and is also sensitive to the relativistic degrees of freedom which affect the expansion rate of the Universe at the radiation-dominated era. At the moment, the most used method of the determination of Y $${}_{p}$$ is the study of the metal deficient H II regions located in blue compact dwarf galaxies (BCDs). In this paper, we discuss in detail various methods of the determination of H II region metallicity in the context of Y $${}_{p}$$ analyses. We show that some procedures used in the methods lead to biases in the metallicity estimates and underestimation of their uncertainties. We propose a modified method for the metallicity determination, as well as an additional criterion for selecting objects. We have selected 69 objects (26 objects with high quality spectra from the HeBCD+NIR database and 43 objects from the SDSS catalog), for which we estimate Y and O/H using the proposed method. We have estimated Y $${}_{p}=0.2470\pm 0.0020$$ which is one of the most accurate estimates obtained up to date. Its comparison with the value Y $${}_{p}=0.2470\pm 0.0002$$ obtained as a result of numerical modelling of Primordial Nucleosynthesis with the value of $$\Omega_{b}$$ taken from the analysis of the CMB anisotropy (Planck mission), is an important tool for studying the self-consistency of the Standard cosmological model (a possible discrepancy between these estimates could be an indicator of a new physics). The application of the proposed method allows one to more correctly estimate Y $${}_{p}$$ and the slope $$d$$ Y/ $$d$$ (O/H). Further analysis of the data from the SDSS catalog can significantly increase the statistics of objects for the regression analysis, which in turn can refine the Y $${}_{p}$$ estimate.