Transfer learning for galaxy morphology from one survey to another
  Deep Learning (DL) algorithms for morphological classification of galaxies
have proven very successful, mimicking (or even improving) visual
classifications. However, these algorithms rely on large training samples of
labelled galaxies (typically thousands of them). A key question for using DL
classifications in future Big Data surveys is how much of the knowledge
acquired from an existing survey can be exported to a new dataset, i.e. if the
features learned by the machines are meaningful for different data. We test the
performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on
Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with
a similar redshift distribution to SDSS. Applying the models directly to DES
data provides a reasonable global accuracy ($\sim$ 90%), but small completeness
and purity values. A fast domain adaptation step, consisting in a further
training with a small DES sample of galaxies ($\sim$500-300), is enough for
obtaining an accuracy > 95% and a significant improvement in the completeness
and purity values. This demonstrates that, once trained with a particular
dataset, machines can quickly adapt to new instrument characteristics (e.g.,
PSF, seeing, depth), reducing by almost one order of magnitude the necessary
training sample for morphological classification. Redshift evolution effects or
significant depth differences are not taken into account in this study.
