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Combining Multi-Cloud Photoionization Models with Multi-Component Bayesian Analysis

Presentation #145.09 in the session Dwarf and Irregular Galaxies — iPoster Session.

Published onJun 29, 2022
Combining Multi-Cloud Photoionization Models with Multi-Component Bayesian Analysis

Photoionization models of emission line galaxies often assume that an identical set of gas clouds is sufficient to reproduce observations. In reality, many observations require the presence of multiple gas clouds with differing physical conditions. Rather than using a discrete set of two or three clouds for this purpose, the local optimally emitting cloud (LOC) model assumes a distribution of clouds are present with different regions contributing varying amounts to the overall emitted spectrum. Moreover, a given galaxy is likely to feature discrete components (e.g. AGN dominated regions and SF dominated regions) where multiple LOC models would apply. We present preliminary results to combine LOC modeling with the multi-component Bayesian analysis code, MULTIGRIS, within the context of the dwarf galaxy dominated RESOLVE survey. We also present multiple possible applications for modeling emission line galaxies including a combination of non-thermally excited and thermally excited clouds, matter bounded and ionization bounded clouds, and /or higher metallicity and lower metallicity clouds. This work is supported by the FACE Foundation Thomas Jefferson Fund, National Science Foundation awards OCI-1053575, and though XSEDE award TG-AST140040.


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