A Generative Model for Disentangling Galaxy Photometric Parameters

• URL: http://arxiv.org/abs/2507.15898v1
• Date: Mon, 21 Jul 2025 03:09:37 GMT
• Title: A Generative Model for Disentangling Galaxy Photometric Parameters
• Authors: Keen Leung, Colen Yan, Jun Yin,
• Abstract summary: We propose a Conditional AutoEncoder (CAE) framework to simultaneously model and characterize galaxy morphology.<n>Our CAE is trained on a suite of realistic mock galaxy images generated via GalSim, encompassing a broad range of galaxy types, photometric parameters, and observational conditions.<n>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.
• Score: 1.8227840589648028
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Category-based Galaxy Image Generation via Diffusion Models [0.39945675027960637]
  We present GalCatDiff, the first framework in astronomy to leverage both galaxy image features and astrophysical properties in the network design of diffusion models.<n>GalCatDiff incorporates an enhanced U-Net and a novel block entitled Astro-RAB (Residual Attention Block), which dynamically combines attention mechanisms with convolution operations to ensure global consistency and local feature fidelity.<n>Our experimental results demonstrate that GalCatDiff significantly outperforms existing methods in terms of the consistency of sample color and size distributions, and the generated galaxies are both visually realistic and physically consistent.
  arXiv  Detail & Related papers  (2025-06-19T12:14:33Z)
• SphereDiffusion: Spherical Geometry-Aware Distortion Resilient Diffusion Model [63.685132323224124]
  Controllable spherical panoramic image generation holds substantial applicative potential across a variety of domains. In this paper, we introduce a novel framework of SphereDiffusion to address these unique challenges. Experiments on Structured3D dataset show that SphereDiffusion significantly improves the quality of controllable spherical image generation and relatively reduces around 35% FID on average.
  arXiv  Detail & Related papers  (2024-03-15T06:26:46Z)
• Spiral-Elliptical automated galaxy morphology classification from telescope images [0.40792653193642503]
  We develop two novel galaxy morphology statistics, descent average and descent variance, which can be efficiently extracted from telescope galaxy images. We utilize the galaxy image data from the Sloan Digital Sky Survey to demonstrate the effective performance of our proposed image statistics.
  arXiv  Detail & Related papers  (2023-10-10T22:36:52Z)
• From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation [0.8010192121024553]
  We present a novel approach for the dimensionality reduction of galaxy images by leveraging a combination of variational auto-encoders (VAE) and domain adaptation (DA) We show that 40-dimensional latent variables can effectively reproduce most morphological features in galaxy images. We further enhance our model by tuning the VAE network via DA using galaxies in the overlapping footprint of DECaLS and BASS+MzLS.
  arXiv  Detail & Related papers  (2023-03-15T13:54:11Z)
• PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification [64.61198351207752]
  Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries. In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology. We propose "Physics Augmented Continuum Neural Radiance Fields" (PAC-NeRF), to estimate both the unknown geometry and physical parameters of highly dynamic objects from multi-view videos.
  arXiv  Detail & Related papers  (2023-03-09T18:59:50Z)
• Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines [116.33694183176617]
  We use a neural network based on the Recurrent Inference Machine to reconstruct an undistorted image of the background source and the lens mass density distribution as pixelated maps. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions.
  arXiv  Detail & Related papers  (2023-01-10T19:00:12Z)
• Towards solving model bias in cosmic shear forward modeling [2.967246997200238]
  Weak gravitational lensing generates a slight shearing of galaxy morphologies called cosmic shear. Modern techniques of shear estimation based on statistics of ellipticity measurements suffer from the fact that the ellipticity is not a well-defined quantity for arbitrary galaxy light profiles. We show that a hybrid physical and deep learning Hierarchical Bayesian Model, where a generative model captures the galaxy morphology, enables us to recover an unbiased estimate of the shear on realistic galaxies.
  arXiv  Detail & Related papers  (2022-10-28T16:23:49Z)
• Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks [70.80563014913676]
  We show that a Bayesian Neural Network (BNN) can be used for the inference, with uncertainty, of such parameters from simulated low-surface-brightness galaxy images. Compared to traditional profile-fitting methods, we show that the uncertainties obtained using BNNs are comparable in magnitude, well-calibrated, and the point estimates of the parameters are closer to the true values.
  arXiv  Detail & Related papers  (2022-07-07T17:55:26Z)
• Realistic galaxy image simulation via score-based generative models [0.0]
  We show that a score-based generative model can be used to produce realistic yet fake images that mimic observations of galaxies. Subjectively, the generated galaxies are highly realistic when compared with samples from the real dataset.
  arXiv  Detail & Related papers  (2021-11-02T16:27:08Z)
• PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting [60.75436852495868]
  We present PhySG, an inverse rendering pipeline that reconstructs geometry, materials, and illumination from scratch from RGB input images. We demonstrate, with both synthetic and real data, that our reconstructions not only enable rendering of novel viewpoints, but also physics-based appearance editing of materials and illumination.
  arXiv  Detail & Related papers  (2021-04-01T17:59:02Z)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.