A Generative Model for Disentangling Galaxy Photometric Parameters
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917170999459840 |
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| author | Leung, Keen Yan, Colen Yin, Jun |
| author_facet | Leung, Keen Yan, Colen Yin, Jun |
| contents | Ongoing and future photometric surveys will produce unprecedented volumes of galaxy images, necessitating robust, efficient methods for deriving galaxy morphological parameters at scale. Traditional approaches, such as parametric light-profile fitting, offer valuable insights but become computationally prohibitive when applied to billions of sources. In this work, we propose a Conditional AutoEncoder (CAE) framework to simultaneously model and characterize galaxy morphology. Our CAE is trained on a suite of realistic mock galaxy images generated via GalSim, encompassing a broad range of galaxy types, photometric parameters (e.g., flux, half-light radius, Sersic index, ellipticity), and observational conditions. By encoding each galaxy image into a low-dimensional latent representation conditioned on key parameters, our model effectively recovers these morphological features in a disentangled manner, while also reconstructing the original image. The results demonstrate that the CAE approach can accurately and efficiently infer complex structural properties, offering a powerful alternative to existing methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_15898 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Generative Model for Disentangling Galaxy Photometric Parameters Leung, Keen Yan, Colen Yin, Jun Instrumentation and Methods for Astrophysics Astrophysics of Galaxies Artificial Intelligence Ongoing and future photometric surveys will produce unprecedented volumes of galaxy images, necessitating robust, efficient methods for deriving galaxy morphological parameters at scale. Traditional approaches, such as parametric light-profile fitting, offer valuable insights but become computationally prohibitive when applied to billions of sources. In this work, we propose a Conditional AutoEncoder (CAE) framework to simultaneously model and characterize galaxy morphology. Our CAE is trained on a suite of realistic mock galaxy images generated via GalSim, encompassing a broad range of galaxy types, photometric parameters (e.g., flux, half-light radius, Sersic index, ellipticity), and observational conditions. By encoding each galaxy image into a low-dimensional latent representation conditioned on key parameters, our model effectively recovers these morphological features in a disentangled manner, while also reconstructing the original image. The results demonstrate that the CAE approach can accurately and efficiently infer complex structural properties, offering a powerful alternative to existing methods. |
| title | A Generative Model for Disentangling Galaxy Photometric Parameters |
| topic | Instrumentation and Methods for Astrophysics Astrophysics of Galaxies Artificial Intelligence |
| url | https://arxiv.org/abs/2507.15898 |