Distributions of the Density and Kinetic Temperature of the Molecular Gas in the Central Region of NGC 613 using Hierarchical Bayesian Inference
Hiroyuki Kaneko, Tomoka Tosaki, Kunihiko Tanaka, Yusuke Miyamoto

TL;DR
This study uses hierarchical Bayesian inference on ALMA molecular line data to map the physical conditions of molecular gas in NGC 613's center, revealing variations in star formation efficiency linked to gas distribution.
Contribution
It applies a novel non-LTE hierarchical Bayesian method to external galaxy data, providing detailed gas property maps and insights into star formation efficiency variations.
Findings
Derived gas densities, temperatures, and column densities consistent with previous studies.
Identified two regions with different star formation efficiencies.
Linked gas deficiency to higher star formation efficiency in one region.
Abstract
We present position-position-velocity (PPV) cubes of the physical and chemical properties of the molecular medium in the central 1.2 kpc region of the active galaxy NGC 613 at a PPV resolution of 0.80.810 km s (0.8 = 68 pc). We used eight molecular lines obtained with ALMA. Non-LTE calculation with hierarchical Bayesian inference was used to construct PPV cubes of the gas kinetic temperature (), molecular hydrogen volume density (), column densities (), and fractional abundances of four molecules (CO, HCN, HCO, and CS). The derived , , and ranged 10 cm, 10 cm, and 10 K, respectively. Our first application of the non-LTE method with the…
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Taxonomy
TopicsSpectroscopy and Laser Applications · Scientific Measurement and Uncertainty Evaluation · Spectroscopy and Chemometric Analyses
