{"ID":2892035,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.15898","arxiv_id":"2507.15898","title":"A Generative Model for Disentangling Galaxy Photometric Parameters","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2507.15898","url_pdf":"https://arxiv.org/pdf/2507.15898v2","authors":"[\"Keen Leung\",\"Colen Yan\",\"Jun Yin\"]","published":"2025-07-21T03:09:37Z","proceeding":"astro-ph.IM","tasks":"[\"astro-ph.IM\",\"astro-ph.GA\",\"cs.AI\"]","methods":"[]","has_code":false}