Practical Galaxy Morphology Tools from Deep Supervised Representation Learning

Understand

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 several recent approaches at practical tasks crucial for investigating large galaxy samples.
• The first task is identifying galaxies of similar morphology to a query galaxy.