The infrared luminosities of ∼332 000 SDSS galaxies predicted from artificial neural networks and the Herschel Stripe 82 survey

• Published in: Monthly notices of the Royal Astronomical Society Vol. 455; no. 1; p. 370
• Main Authors: Ellison, Sara L, Teimoorinia, Hossein, Rosario, David J, Mendel, J Trevor
• Format: Journal Article
• Language: English
• Published: London Oxford University Press 01.01.2016
• Subjects: Active galactic nuclei; Artificial neural networks; Astronomy; Galaxies; Infrared; Infrared radiation; Learning theory; Luminosity; Neural networks; Sky surveys (astronomy); Stars & galaxies; Universe
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1093/mnras/stv2275

The total infrared (IR) luminosity (L sub(IR)) can be used as a robust measure of a galaxy's star formation rate (SFR), even in the presence of an active galactic nucleus (AGN), or when optical emission lines are weak. Unfortunately, existing all sky far-IR surveys, such as the Infrared Astronomical Satellite (IRAS) and AKARI, are relatively shallow and are biased towards the highest SFR galaxies and lowest redshifts. More sensitive surveys with the Herschel Space Observatory are limited to much smaller areas. In order to construct a large sample of L sub(IR) measurements for galaxies in the nearby Universe, we employ artificial neural networks (ANNs), using 1136 galaxies in the Herschel Stripe 82 sample as the training set. The networks are validated using two independent data sets (IRAS and AKARI) and demonstrated to predict the L sub(IR) with a scatter sigma similar to 0.23 dex, and with no systematic offset. Importantly, the ANN performs well for both star-forming galaxies and those with an AGN. A public catalogue is presented with our L sub(IR) predictions which can be used to determine SFRs for 331 926 galaxies in the Sloan Digital Sky Survey (SDSS), including ~129 000 SFRs for AGN-dominated galaxies for which SDSS SFRs have large uncertainties.