Star Formation Rates for photometric samples of galaxies using machine
  learning methods
  Star Formation Rates or SFRs are crucial to constrain theories of galaxy
formation and evolution. SFRs are usually estimated via spectroscopic
observations requiring large amounts of telescope time. We explore an
alternative approach based on the photometric estimation of global SFRs for
large samples of galaxies, by using methods such as automatic parameter space
optimisation, and supervised Machine Learning models. We demonstrate that, with
such approach, accurate multi-band photometry allows to estimate reliable SFRs.
We also investigate how the use of photometric rather than spectroscopic
redshifts, affects the accuracy of derived global SFRs. Finally, we provide a
publicly available catalogue of SFRs for more than 27 million galaxies
extracted from the Sloan Digital Sky survey Data Release 7. The catalogue is
available through the Vizier facility at the following link
ftp://cdsarc.u-strasbg.fr/pub/cats/J/MNRAS/486/1377.
