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Predicting the global far-infrared emission of galaxies

Published online by Cambridge University Press:  10 June 2020

Wouter Dobbels
Affiliation:
Sterrenkundig Observatorium, Universiteit Gent, Krijgslaan 281, B-9000 Gent, Belgium email: wouter.dobbels@ugent.be
Maarten Baes
Affiliation:
Sterrenkundig Observatorium, Universiteit Gent, Krijgslaan 281, B-9000 Gent, Belgium email: wouter.dobbels@ugent.be
Corresponding
E-mail address:

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.

Type
Contributed Papers
Copyright
© International Astronomical Union 2020

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