Domain adaptation in application to gravitational lens finding
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912055265591296 |
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| author | Parul, Hanna Gleyzer, Sergei Reddy, Pranath Toomey, Michael W. |
| author_facet | Parul, Hanna Gleyzer, Sergei Reddy, Pranath Toomey, Michael W. |
| contents | The next decade is expected to see a tenfold increase in the number of strong gravitational lenses, driven by new wide-field imaging surveys. To discover these rare objects, efficient automated detection methods need to be developed. In this work, we assess the performance of three domain adaptation techniques -- Adversarial Discriminative Domain Adaptation (ADDA), Wasserstein Distance Guided Representation Learning (WDGRL), and Supervised Domain Adaptation (SDA) -- in enhancing lens-finding algorithms trained on simulated data when applied to observations from the Hyper Suprime-Cam Subaru Strategic Program. We find that WDGRL combined with an ENN-based encoder provides the best performance in an unsupervised setting and that supervised domain adaptation is able to enhance the model's ability to distinguish between lenses and common similar-looking false positives, such as spiral galaxies, which is crucial for future lens surveys. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01203 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Domain adaptation in application to gravitational lens finding Parul, Hanna Gleyzer, Sergei Reddy, Pranath Toomey, Michael W. Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics The next decade is expected to see a tenfold increase in the number of strong gravitational lenses, driven by new wide-field imaging surveys. To discover these rare objects, efficient automated detection methods need to be developed. In this work, we assess the performance of three domain adaptation techniques -- Adversarial Discriminative Domain Adaptation (ADDA), Wasserstein Distance Guided Representation Learning (WDGRL), and Supervised Domain Adaptation (SDA) -- in enhancing lens-finding algorithms trained on simulated data when applied to observations from the Hyper Suprime-Cam Subaru Strategic Program. We find that WDGRL combined with an ENN-based encoder provides the best performance in an unsupervised setting and that supervised domain adaptation is able to enhance the model's ability to distinguish between lenses and common similar-looking false positives, such as spiral galaxies, which is crucial for future lens surveys. |
| title | Domain adaptation in application to gravitational lens finding |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2410.01203 |