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Self-supervised Representation Learning for Astronomical Images
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AI
DOI：10.3847/2041-8213/abf2c7.png)
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摘要
En 中文
Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks. These representations can be directly used as features, or fine-tuned, to outperform supervised methods trained only on labeled data. We apply a contrastive learning framework on multiband galaxy photometry from the Sloan Digital Sky Survey (SDSS), to learn image representations. We then use them for galaxy morphology classification and fine-tune them for photometric redshift estimation, using labels from the Galaxy Zoo 2 data set and SDSS spectroscopy. In both downstream tasks, using the same learned representations, we outperform the supervised state-of-the-art results, and we show that our approach can achieve the accuracy of supervised models while using 2-4 times fewer labels for training. The codes, trained models, and data can be found at https://portal.nersc.gov/ project/dasrepo/self-supervised-learning-sdss.
Keyword:
DIGITAL SKY SURVEY
GALAXY ZOO
PHOTOMETRIC REDSHIFTS
MORPHOLOGY
SDSS
CLASSIFICATION
期刊
IF:
11.7
论文数:
1.2W
被引数:
5.9W
机构
引用论文
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OAAI分享收藏MEASURING REDDENING WITH SLOAN DIGITAL SKY SURVEY STELLAR SPECTRA AND RECALIBRATING SFD使用SLOAN DIGITAL SKY SURVEY恒星光谱测量变红并重新校准SFDOAAI
OAAI分享收藏Spectroscopic target selection in the Sloan Digital Sky Survey: The main galaxy sample.png)
ASTRONOMICAL JOURNALOAAI
ASTRONOMICAL JOURNALIF5.1
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