The infra-red luminosities of ~332,000 SDSS galaxies predicted from
  artificial neural networks and the Herschel Stripe 82 survey
  The total infra-red (IR) luminosity (L_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 Infra-red 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
datasets (IRAS and AKARI) and demonstrated to predict the L_IR with a scatter
sigma ~ 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 catalog is
presented with our L_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.
