Predicted LIR for SDSS galaxies

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_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_IR_ with a scatter {sigma}~0.23dex, and with no systematic offset. Importantly, the ANN performs well for both star-forming galaxies and those with an AGN.