Detailed Record



PHANGS-ML: Dissecting Multiphase Gas and Dust in Nearby Galaxies Using Machine Learning


Abstract The PHANGS survey uses Atacama Large Millimeter/submillimeter Array, Hubble Space Telescope, Very Large Telescope, and JWST to obtain an unprecedented high-resolution view of nearby galaxies, covering millions of spatially independent regions. The high dimensionality of such a diverse multiwavelength data set makes it challenging to identify new trends, particularly when they connect observables from different wavelengths. Here, we use unsupervised machine-learning algorithms to mine this information-rich data set to identify novel patterns. We focus on three of the PHANGS-JWST galaxies, for which we extract properties pertaining to their stellar populations; warm ionized and cold molecular gas; and polycyclic aromatic hydrocarbons (PAHs), as measured over 150 pc scale regions. We show that we can divide the regions into groups with distinct multiphase gas and PAH properties. In the process, we identify previously unknown galaxy-wide correlations between PAH band and optical line ratios and use our identified groups to interpret them. The correlations we measure can be naturally explained in a scenario where the PAHs and the ionized gas are exposed to different parts of the same radiation field that varies spatially across the galaxies. This scenario has several implications for nearby galaxies: (i) The uniform PAH ionized fraction on 150 pc scales suggests significant self-regulation in the interstellar medium, (ii) the PAH 11.3/7.7 μ m band ratio may be used to constrain the shape of the non-ionizing far-ultraviolet to optical part of the radiation field, and (iii) the varying radiation field affects line ratios that are commonly used as PAH size diagnostics. Neglecting this effect leads to incorrect or biased PAH sizes.
Authors Dalya Baron ORCID , Karin Sandström ORCID , Erik Rosolowsky ORCID , Oleg V. Egorov ORCID , Ralf S. Klessen ORCID , Adam K. Leroy ORCID , M. Boquien ORCID , E. Schinnerer ORCID , Francesco Belfiore ORCID , Brent Groves ORCID , Jérémy Chastenet ORCID , Daniel A. Dale University of WyomingORCID , Guillermo A. Blanc ORCID , J. E. Méndez-Delgado ORCID , Eric W. Koch ORCID , Kathryn Grasha ORCID , Mélanie Chevance ORCID , David A. Thilker ORCID , Dario Colombo ORCID , Thomas G. Williams ORCID , Debosmita Pathak ORCID , Jessica Sutter ORCID , Toby Brown ORCID , John F. Wu ORCID , J. E. G. Peek ORCID , Éric Emsellem ORCID , Kirsten L. Larson ORCID , Justus Neumann ORCID
Journal Info IOP Publishing | The Astrophysical Journal , vol: 968 , iss: 1 , pages: 24 - 24
Publication Date 6/1/2024
ISSN 0004-637X
TypeKeyword Image article
Open Access gold Gold Access
DOI https://doi.org/10.3847/1538-4357/ad39e5
KeywordsKeyword Image Multi-Object Spectroscopy (Score: 0.508962)