The interstellar medium of star-forming galaxies is characterized by the presence of a wide variety of nebulae of ionized gas which are usually associated with three main classes of objects: Hii regions, planetary nebulae, and supernova remnants. They are the results of different physical processes, hence they trace different properties (e.g., star formation rate, chemical abundances, feedback), and they all are essential to study the structure and evolution of their host galaxy. Being able to associate each nebula to its class is, however, critical for a proper analysis. For this reason, during the years, many classification methods have been successfully developed. However, most of them are strongly dataset dependent and, while they are qualitatively in agreement with each other, the quantitative constraints often are relatively subjective.
Instruments like MUSE@VLT and surveys like the PHANGS-MUSE survey, which observed the star-forming disk of 19 nearby star-forming galaxies with MUSE, are opening unprecedented possibilities to study ionized nebulae and to define new classification methods. In this talk, I will present the first results of my work aimed at using the PHANGS-MUSE data to compile the largest catalog of ionized nebulae produced so far. To do so, I am developing a new classification algorithm that uses the Bayesian concept of model comparison to classify the detected regions reliably and objectively while also taking advantage of the spectral and spatial information that can be recovered from MUSE datacubes.
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