Simultaneously modelling far-infrared dust emission and its relation to CO emission in star forming galaxies
Rahul Shetty, Julia Roman-Duval, Sacha Hony, Diane Cormier, Ralf S., Klessen, Lukas K. Konstandin, Thomas Loredo, Eric W. Pellegrini, David, Ruppert

TL;DR
This paper introduces a hierarchical Bayesian method to simultaneously model dust far-infrared emission and its relation to CO emission in star-forming galaxies, providing insights into the ISM properties and their variations.
Contribution
The novel hierarchical Bayesian approach accurately estimates dust and gas properties and their relationship from FIR and CO data, outperforming traditional non-hierarchical methods.
Findings
Similar IR-CO slopes in LMC and SMC, with higher intercept in SMC due to lower metallicity.
Increase in dust temperature in dense molecular regions of the LMC.
Method successfully models dust and gas properties on 100 pc scales in nearby galaxies.
Abstract
We present a method to simultaneously model the dust far-infrared spectral energy distribution (SED) and the total infrared carbon monoxide (CO) integrated intensity relationship. The modelling employs a hierarchical Bayesian (HB) technique to estimate the dust surface density, temperature (), and spectral index at each pixel from the observed far-infrared (FIR) maps. Additionally, given the corresponding CO map, the method simultaneously estimates the slope and intercept between the FIR and CO intensities, which are global properties of the observed source. The model accounts for correlated and uncorrelated uncertainties, such as those present in Herschel observations. Using synthetic datasets, we demonstrate the accuracy of the HB method, and contrast the results with common non-hierarchical fitting methods. As an initial application, we…
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