Deriving model-based Te-consistent chemical abundances in ionized gaseous nebulae

DOI: 
10.1093/mnras/stu753
Publication date: 
01/07/2014
Main author: 
Pérez-Montero E.
IAA authors: 
Pérez-Montero E.
Authors: 
Pérez-Montero E.
Journal: 
Monthly Notices of the Royal Astronomical Society
Publication type: 
Article
Volume: 
441
Pages: 
2663-2675
Number: 
Abstract: 
The derivation of abundances in gaseous nebulae ionized by massive stars using optical collisionally excited emission lines is studied in this work, comparing the direct or Te method with updated grids of photoionization models covering a wide range of input conditions of O/H and N/O abundances and ionization parameter. The abundances in a large sample of compiled objects with at least one auroral line are re-derived and later compared with the χ2-weighted-mean abundances from the models. The agreement between the abundances using the two methods both for O/H and N/O is excellent with no additional assumptions about the geometry or physics governing the HII regions. Although very inaccurate model-based O/H are obtained when no auroral lines are considered, this can be overcome assuming empirical laws between O/H, log U, and N/O to constrain the considered models. In this way, for 12+log(O/H) > 8.0, a precision better than 0.1 dex consistent with the direct method is attained. For very low Z, models give higher O/H values and a high dispersion, possibly owing to the contamination of the low-excitation emission lines. However, in this regime, the auroral lines are usually well detected. The use of this procedure, in a publicly available script, HII-CHI-MISTRY, leads to the derivation of abundances in faint-/high-redshift objects consistent with the direct method based on collisionally excited lines. © 2014 The Author. Published by Oxford University Press on behalf of the Royal Astronomical Society.
Database: 
WOK
SCOPUS
ADS
URL: 
https://ui.adsabs.harvard.edu/#abs/2014MNRAS.441.2663P/abstract
ADS Bibcode: 
2014MNRAS.441.2663P
Keywords: 
Galaxies: Abundances; ISM: Abundances; Methods: Data analysis