Efficient galaxy classification through pretraining

Abstract

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Deep learning has increasingly been applied to supervised learning tasks in astronomy, such as classifying images of galaxies based on their apparent shape (i.e., galaxy morphology classification) to gain insight regarding the evolution of galaxies. In this work, we examine the effect of pretraining on the performance of the classical AlexNet convolutional neural network (CNN) in classifying images of 14,034 galaxies from the Sloan Digital Sky Survey Data Release 4. Pretraining involves designing and training CNNs on large labeled image datasets unrelated to astronomy, which takes advantage of the vast amounts of such data available compared to the relatively small amount of labeled galaxy images. We show a statistically significant benefit of using pretraining, both in terms of improved overall classification success and reduced computational cost to achieve such performance.

Keywords

• convolutional neural networks
• machine learning
• galaxy morphology
• astrostatistics
• transfer learning
• pretraining