Practical galaxy morphology tools from deep supervised representation learning
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Oxford University Press
Abstract
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. 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 per cent 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).
Description
Keywords
Software, Astrophysics, Data handling, Morphology, Galaxies
Citation
Walmsley, M. et al. (2022). Practical galaxy morphology tools from deep supervised representation learning. Monthly Notices of the Royal Astronomical Society, 513(2), 1581-1599. 10.1093/mnras/stac525