Galaxy Morphology Classification with Counterfactual Explanation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912652744196096 |
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| author | Cao, Zhuo Krieger, Lena Scharr, Hanno Assent, Ira |
| author_facet | Cao, Zhuo Krieger, Lena Scharr, Hanno Assent, Ira |
| contents | Galaxy morphologies play an essential role in the study of the evolution of galaxies. The determination of morphologies is laborious for a large amount of data giving rise to machine learning-based approaches. Unfortunately, most of these approaches offer no insight into how the model works and make the results difficult to understand and explain. We here propose to extend a classical encoder-decoder architecture with invertible flow, allowing us to not only obtain a good predictive performance but also provide additional information about the decision process with counterfactual explanations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14655 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Galaxy Morphology Classification with Counterfactual Explanation Cao, Zhuo Krieger, Lena Scharr, Hanno Assent, Ira Machine Learning Artificial Intelligence Galaxy morphologies play an essential role in the study of the evolution of galaxies. The determination of morphologies is laborious for a large amount of data giving rise to machine learning-based approaches. Unfortunately, most of these approaches offer no insight into how the model works and make the results difficult to understand and explain. We here propose to extend a classical encoder-decoder architecture with invertible flow, allowing us to not only obtain a good predictive performance but also provide additional information about the decision process with counterfactual explanations. |
| title | Galaxy Morphology Classification with Counterfactual Explanation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2510.14655 |