# A Generative Model for Disentangling Galaxy Photometric Parameters

arXiv: https://arxiv.org/abs/2507.15898 · HTML: https://mlsanity.com/paper/2507.15898/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [A guide to convolution arithmetic for deep learning](https://mlsanity.com/paper/1603.07285/) (2016)
- [Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders](https://mlsanity.com/paper/1703.02435/) (2017)
- [Autoencoders for unsupervised anomaly detection in high energy physics](https://mlsanity.com/paper/2104.09051/) (2021)
- [Improving Chemical Autoencoder Latent Space and Molecular De novo Generation Diversity with Heteroencoders](https://mlsanity.com/paper/1806.09300/) (2018)
- [Convolutional Deep Denoising Autoencoders for Radio Astronomical Images](https://mlsanity.com/paper/2110.08618/) (2021)
- [A Closer Look at Disentangling in β-VAE](https://mlsanity.com/paper/1912.05127/) (2019)
- [Non-equilibrium physics: from spin glasses to machine and neural learning](https://mlsanity.com/paper/2308.01538/) (2023)
- [Detecting subtle macroscopic changes in a finite temperature classical scalar field with machine learning](https://mlsanity.com/paper/2311.12303/) (2023)

## Abstract (arXiv)

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.