On the origin of optical and IR emission lines in star forming galaxies
Combining the {\sc Cloudy} photoionization code with updated stellar population synthesis results, we simultaneously model the MIR $\neiii/\neii$ vs. $\oiv/\neiii$, the MIR-FIR $\neiii/\neii$ vs. $\oiv/\oiii$ and the classical BPT diagnostic diagrams. We focus on the properties of optically classifi...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
10.08.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Combining the {\sc Cloudy} photoionization code with updated stellar
population synthesis results, we simultaneously model the MIR $\neiii/\neii$
vs. $\oiv/\neiii$, the MIR-FIR $\neiii/\neii$ vs. $\oiv/\oiii$ and the
classical BPT diagnostic diagrams. We focus on the properties of optically
classified \hii\,galaxies that lie in the normal star forming zone in the MIR
diagnostic diagram. We find that a small fraction of our models lie in this
zone, but most of them correspond to the lowest explored metallicity,
\zstar\,=\,0.0002, at age $\sim1$ Gyr. This value of \zstar\,is, by far, lower
than the values derived for these galaxies from optical emission lines,
suggesting that the far-UV emission produced by post-AGB stars (a.k.a. HOLMES,
hot low-mass evolved stars) is NOT the source of ionization. Instead, shock
models can easily reproduce this part of the MIR diagram. We suggest that it is
likely that some of these galaxies have been misclassified and that in them,
shocks, produced by a weak AGN-outflow, could be an important source of
ionizaton. Using a subset of our models, we derive a new demarcation line for
the maximal contribution of retired galaxies in the BPT diagram. This
demarcation line allows for a larger contamination from the neighbouring
AGN-dominated region. Considering the importance of disentangling the different
ionising mechanisms in weak or deeply obscured systems, new observational
efforts to classify galaxies both in the optical and IR are required to better
constrain this kind of models and understand their evolutionary paths. |
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DOI: | 10.48550/arxiv.2308.05604 |