Predicting the global far-infrared emission of galaxies. Issue Volume 15:Issue S341(2019) (November 2019)

• Record Type: Journal Article
• Authors: Dobbels, Wouter; Baes, Maarten
• Editors: Boquien, Médéric; Lusso, Elisabeta; Gruppioni, Carlotta; Tissera, Patricia
• Is Part Of: Proceedings of the International Astronomical Union. Volume 15:Issue S341(2019)
• Page Start: 114
• Page End: 118
• Publication Date: 2019-11
• Subjects: infrared: galaxies, -- infrared: ISM, -- galaxies: ISM; Astronomy -- Congresses; Astronomy -- Periodicals; 520
• DOI: 10.1017/S1743921319002515
• Languages: English
• ISSNs: 1743-9213

Abstract

Abstract: Dust absorbs stellar emission and reradiates this energy in the far-infrared (FIR). FIR observations hence give us a direct view of the dust, and allow us to study its properties. Unfortunately, FIR observations are only available for a small subset of galaxies. In this work, we estimate the global FIR emission from global UV-NIR observations. We show that a machine learning method clearly outperforms a SED modelling approach. For each galaxy, we not only predict the FIR flux across the 6 Herschel bands, but also estimate individual uncertainties. We inspect the worst predictions, and investigate how the machine learning predictor generalizes on new data. Our predictor can be used as a virtual observatory, which is especially useful now that there is still no confirmed next-generation FIR telescope.