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The total infra-red (IR) luminosity (L_IR) can be used as a robust measure ofa galaxy's star formation rate (SFR), even in the presence of an activegalactic 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 SFRgalaxies and lowest redshifts. More sensitive surveys with the Herschel SpaceObservatory are limited to much smaller areas. In order to construct a largesample of L_IR measurements for galaxies in the nearby universe, we employartificial neural networks (ANNs), using 1136 galaxies in the Herschel Stripe82 sample as the training set. The networks are validated using two independentdatasets (IRAS and AKARI) and demonstrated to predict the L_IR with a scattersigma ~ 0.23 dex, and with no systematic offset. Importantly, the ANN performswell for both star-forming galaxies and those with an AGN. A public catalog ispresented with our L_IR predictions which can be used to determine SFRs for331,926 galaxies in the Sloan Digital Sky Survey (SDSS), including ~ 129,000SFRs for AGN-dominated galaxies for which SDSS SFRs have large uncertainties.

The infra-red luminosities of ~332,000 SDSS galaxies predicted from artificial neural networks and the Herschel Stripe 82 survey