Practical Galaxy Morphology Tools from Deep Supervised Representation
  Learning
  Astronomers have typically set out to solve supervised machine learning
problems by creating their own representations from scratch. We show that deep
learning models trained to answer every Galaxy Zoo DECaLS question learn
meaningful semantic representations of galaxies that are useful for new tasks
on which the models were never trained. We exploit these representations to
outperform existing approaches at several practical tasks crucial for
investigating large galaxy samples. The first task is identifying galaxies of
similar morphology to a query galaxy. Given a single galaxy assigned a free
text tag by humans (e.g. `#diffuse'), we can find galaxies matching that tag
for most tags. The second task is identifying the most interesting anomalies to
a particular researcher. Our approach is 100\% accurate at identifying the most
interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third
task is adapting a model to solve a new task using only a small number of
newly-labelled galaxies. Models fine-tuned from our representation are better
able to identify ring galaxies than models fine-tuned from terrestrial images
(ImageNet) or trained from scratch. We solve each task with very few new
labels; either one (for the similarity search) or several hundred (for anomaly
detection or fine-tuning). This challenges the longstanding view that deep
supervised methods require new large labelled datasets for practical use in
astronomy. To help the community benefit from our pretrained models, we release
our fine-tuning code zoobot. Zoobot is accessible to researchers with no prior
experience in deep learning.
