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arXiv · 1910.14056

Unsupervised Star Galaxy Classification with Cascade Variational Auto-Encoder

Hao Sun·Jiadong Guo·Edward J. Kim·Robert J. Brunner
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Abstract

The increasing amount of data in astronomy provides great challenges for machine learning research. Previously, supervised learning methods achieved satisfactory recognition accuracy for the star-galaxy classification task, based on manually labeled data set. In this work, we propose a novel unsupervised approach for the star-galaxy recognition task, namely Cascade Variational Auto-Encoder (CasVAE). Our empirical results show our method outperforms the baseline model in both accuracy and stability.

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Hao Sun, Jiadong Guo, Edward J. Kim, Robert J. Brunner. 2019-10-30. Unsupervised Star Galaxy Classification with Cascade Variational Auto-Encoder. https://arxiv.org/abs/1910.14056

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