Predicting the global far-infrared SED of galaxies via machine learning techniques^⋆

¹ Sterrenkundig Observatorium, Universiteit Gent, Krijgslaan 281, 9000 Gent, Belgium
e-mail: wouter.dobbels@ugent.be
² Centre for Astrophysics Research, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK
³ INAF – Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125 Florence, Italy
⁴ School of Physics & Astronomy, Cardiff University, Queen’s Buildings, The Parade, Cardiff CF24 3AA, UK
⁵ INAF – Istituto di Radioastronomia, Via P. Gobetti 101, 4019 Bologna, Italy
⁶ Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, Maryland 21218, USA
⁷ Instituto de Radioastronomía y Astrofísica, UNAM, Campus Morelia, A. P. 3-72, CP 58089 Morelia, Mexico
⁸ AIM, CEA, CNRS, Université Paris-Saclay, Université Paris Diderot, Sorbonne Paris Cité, 91191 Gif-sur-Yvette, France
⁹ Central Astronomical Observatory of RAS, Pulkovskoye Chaussee 65/1, 196140 St. Petersburg, Russia
¹⁰ St. Petersburg State University, Universitetskij Pr. 28, 198504 St. Petersburg, Stary Peterhof, Russia
¹¹ National Observatory of Athens, Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, Ioannou Metaxa and Vasileos Pavlou, 15236 Athens, Greece
¹² Department of Astrophysics, Astronomy & Mechanics, Faculty of Physics, University of Athens, Panepistimiopolis, 15784 Zografos, Athens, Greece

Received: 13 September 2019
Accepted: 4 October 2019

Abstract

Context. Dust plays an important role in shaping a galaxy’s spectral energy distribution (SED). It absorbs ultraviolet (UV) to near-infrared radiation and re-emits this energy in the far-infrared (FIR). The FIR is essential to understand dust in galaxies. However, deep FIR observations require a space mission, none of which are still active today.

Aims. We aim to infer the FIR emission across six Herschel bands, along with dust luminosity, mass, and effective temperature, based on the available UV to mid-infrared (MIR) observations. We also want to estimate the uncertainties of these predictions, compare our method to energy balance SED fitting, and determine possible limitations of the model.

Methods. We propose a machine learning framework to predict the FIR fluxes from 14 UV–MIR broadband fluxes. We used a low redshift sample by combining DustPedia and H-ATLAS, and extracted Bayesian flux posteriors through SED fitting. We trained shallow neural networks to predict the far-infrared fluxes, uncertainties, and dust properties. We evaluated them on a test set using a root mean square error (RMSE) in log-space.

Results. Our results (RMSE = 0.19 dex) significantly outperform UV–MIR energy balance SED fitting (RMSE = 0.38 dex), and are inherently unbiased. We can identify when the predictions are off, for example when the input has large uncertainties on WISE 22 μm, or when the input does not resemble the training set.

Conclusions. The galaxies for which we have UV–FIR observations can be used as a blueprint for galaxies that lack FIR data. This results in a “virtual FIR telescope”, which can be applied to large optical-MIR galaxy samples. This helps bridge the gap until the next FIR mission.