Star formation rates and stellar masses from machine learning
  Star-formation activity is a key property to probe the structure formation
and hence characterise the large-scale structures of the universe. This
information can be deduced from the star formation rate (SFR) and the stellar
mass (Mstar), both of which, but especially the SFR, are very complex to
estimate. Determining these quantities from UV, optical, or IR luminosities
relies on complex modeling and on priors on galaxy types. We propose a method
based on the machine-learning algorithm Random Forest to estimate the SFR and
the Mstar of galaxies at redshifts in the range 0.01<z<0.3, independent of
their type. The machine-learning algorithm takes as inputs the redshift, WISE
luminosities, and WISE colours in near-IR, and is trained on spectra-extracted
SFR and Mstar from the SDSS MPA-JHU DR8 catalogue as outputs. We show that our
algorithm can accurately estimate SFR and Mstar with scatters of sigma_SFR=0.38
dex and sigma_Mstar=0.16 dex for SFR and stellar mass, respectively, and that
it is unbiased with respect to redshift or galaxy type. The full-sky coverage
of the WISE satellite allows us to characterise the star-formation activity of
all galaxies outside the Galactic mask with spectroscopic redshifts in the
range 0.01<z<0.3. The method can also be applied to photometric-redshift
catalogues, with best scatters of sigma_SFR=0.42 dex and sigma_Mstar=0.24 dex
obtained in the redshift range 0.1<z<0.3.
