Morphological classification of astronomical images with limited labelling

• URL: http://arxiv.org/abs/2105.02958v1
• Date: Tue, 27 Apr 2021 19:26:27 GMT
• Title: Morphological classification of astronomical images with limited labelling
• Authors: Andrey Soroka (1), Alex Meshcheryakov (2), Sergey Gerasimov (1) ((1) Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University, (2) Space Research Institute of RAS)
• Abstract summary: We propose an effective semi-supervised approach for galaxy morphology classification task, based on active learning of adversarial autoencoder (AAE) model. For a binary classification problem (top level question of Galaxy Zoo 2 decision tree) we achieved accuracy 93.1% on the test part with only 0.86 millions markup actions. Our best model with additional markup accuracy of 95.5%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The task of morphological classification is complex for simple parameterization, but important for research in the galaxy evolution field. Future galaxy surveys (e.g. EUCLID) will collect data about more than a $10^9$ galaxies. To obtain morphological information one needs to involve people to mark up galaxy images, which requires either a considerable amount of money or a huge number of volunteers. We propose an effective semi-supervised approach for galaxy morphology classification task, based on active learning of adversarial autoencoder (AAE) model. For a binary classification problem (top level question of Galaxy Zoo 2 decision tree) we achieved accuracy 93.1% on the test part with only 0.86 millions markup actions, this model can easily scale up on any number of images. Our best model with additional markup achieves accuracy of 95.5%. To the best of our knowledge it is a first time AAE semi-supervised learning model used in astronomy.

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