Abstract

High-ionization star-forming (SF) galaxies are easily identified with strong emission-line techniques such as the BPT diagram, and form an obvious ionization sequence on such diagrams. We use a locally optimally emitting cloud model to fit emission-line ratios that constrain the excitation mechanism, spectral energy distribution, abundances and physical conditions along the star formation ionization sequence. Our analysis takes advantage of the identification of a sample of pure SF galaxies, to define the ionization sequence, via mean field independent component analysis. Previous work has suggested that the major parameter controlling the ionization level in SF galaxies is the metallicity. Here we show that the observed SF sequence could alternatively be interpreted primarily as a sequence in the distribution of the ionizing flux incident on gas spread throughout a galaxy. Metallicity variations remain necessary to model the SF sequence, however, our best models indicate that galaxies with the highest and lowest observed ionization levels (outside the range –0.37 < log [O III]/Hβ < –0.09) require the variation of an additional physical parameter other than metallicity, which we determine to be the distribution of ionizing flux in the galaxy.

Document Type

Article

Publication Date

3-11-2016

Notes/Citation Information

Published in Monthly Notices of the Royal Astronomical Society, v. 458, issue 1, p. 988-1012.

This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2016 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.

The copyright holders have granted the permission for posting the article here.

Digital Object Identifier (DOI)

https://doi.org/10.1093/mnras/stw100

Funding Information

CTR wishes to acknowledge the support of the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number OCI-1053575, and Elon University's FR&D Summer Research Fellowship. The Michigan State University High Performance Computing Center (HPCC) and the Institute for Cyber Enabled Research (ICER) also supported this work. CTR and JAB acknowledge NSF support for this work under grant AST-1006593. CTR and HM wish to acknowledge the support of Elon University's Lumen Prize, Summer Undergraduate Research Experience (SURE), and Honors Program. JTA acknowledges the award of a SIEF John Stocker Fellowship. PCH acknowledges the support of the UK Science and Technology Research Council (STFC). GJF acknowledges support by NSF (1108928, 1109061, and 1412155), NASA (10-ATP10-0053, 10-ADAP10-0073, NNX12AH73G, and ATP13-0153), and STScI (HST-AR- 13245, GO-12560, HST-GO-12309, GO-13310.002-A, and HST-AR-13914). AC acknowledges Elon University for his sabbatical leave.

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