Predicting far-infrared maps of galaxies via machine learning techniques

Abstract

The ultraviolet (UV) to sub-millimetre (submm) spectral energy distribution of galaxies can be roughly divided into two sections: the stellar emission (attenuated by dust) at UV to near-infrared wavelengths and dust emission at longer wavelengths. In Dobbels et al. (2020), we show that these two sections are strongly related, and we can predict the global dust properties from the integrated UV to mid-infrared emission with the help of machine learning techniques. We investigate if these machine learning techniques can also be extended to resolved scales. Our aim is to predict resolved maps of the specific dust luminosity, specific dust mass, and dust temperature starting from a set of surface brightness images from UV to mid-infrared wavelengths. We used a selection of nearby galaxies retrieved from the DustPedia sample, in addition to M31 and M33. These were convolved and resampled to a range of pixel sizes, ranging from 150 pc to 3 kpc. We trained a random forest model which considers each pixel individually. We find that the predictions work well on resolved scales, with the dust mass and temperature having a similar root mean square error as on global scales (0.32 dex and 3.15 K on 18" scales respectively), and the dust luminosity being noticeably better (0.11 dex). We find no significant dependence on the pixel scale. Predictions on individual galaxies can be biased, and we find that about two-thirds of the scatter can be attributed to scatter between galaxies (rather than within galaxies). A machine learning approach can be used to create dust maps, with its resolution being only limited to the input bands, thus achieving a higher resolution than Herschel. These dust maps can be used to improve global estimates of dust properties, they can lead to a better estimate of dust attenuation, and they can be used as a constraint on cosmological simulations that trace dust.

16 pages, 13 figures, accepted by A&A

References in corpus (21)

• The EAGLE project: Simulating the evolution and assembly of galaxies and their environments
• UV Star Formation Rates in the Local Universe
• A simple model to interpret the ultraviolet, optical and infrared emission from galaxies
• Simba: Cosmological Simulations with Black Hole Growth and Feedback
• HyperLEDA. III. The catalogue of extragalactic distances
• GALEX-SDSS-WISE Legacy Catalog (GSWLC): Star Formation Rates, Stellar Masses and Dust Attenuations of 700,000 Low-redshift Galaxies
• DustPedia - A Definitive Study of Cosmic Dust in the Local Universe
• Old and young stellar populations in DustPedia galaxies and their role in dust heating
• The Herschel Exploitation of Local Galaxy Andromeda (HELGA) VII: A SKIRT radiative transfer model and insights on dust heating
• Missing Stellar Mass in SED Fitting: Spatially Unresolved Photometry can Underestimate Galaxy Masses
• The identification of dust heating mechanisms in nearby galaxies using Herschel 160/250 and 250/350 micron surface brightness ratios
• The Resolved Distributions of Dust Mass and Temperature in Local Group Galaxies
• High-Resolution Radiative Transfer Modelling of M33
• High-resolution, 3D radiative transfer modelling III. The DustPedia barred galaxies
• Predicting star formation properties of galaxies using deep learning
• High-resolution synthetic UV-submm images for simulated Milky Way-type galaxies from the Auriga project
• High-resolution, 3D radiative transfer modelling. II. The early-type spiral galaxy M81
• Nonparametric galaxy morphology from UV to submm wavelengths
• High-resolution, 3D radiative transfer modelling IV. AGN-powered dust heating in NGC 1068
• Predicting the global far-infrared SED of galaxies via machine learning techniques
• Morphology-assisted galaxy mass-to-light predictions using deep learning