# Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models

arXiv: https://arxiv.org/abs/2406.18175 · HTML: https://mlsanity.com/paper/2406.18175/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 130 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 1 of 1)

- [ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection](https://mlsanity.com/paper/2208.10984/) (2022)

## Built on by (most cited first, 2 of 2)

- [Insights on Galaxy Evolution from Interpretable Sparse Feature Networks](https://mlsanity.com/paper/2501.00089/) (2024)
- [Mapping Galaxy Images Across Ultraviolet, Visible and Infrared Bands Using Generative Deep Learning](https://mlsanity.com/paper/2501.15149/) (2025)

## Abstract (arXiv)

Modern spectroscopic surveys can only target a small fraction of the vast amount of photometrically cataloged sources in wide-field surveys. Here, we report the development of a generative AI method capable of predicting optical galaxy spectra from photometric broad-band images alone. This method draws from the latest advances in diffusion models in combination with contrastive networks. We pass multi-band galaxy images into the architecture to obtain optical spectra. From these, robust values for galaxy properties can be derived with any methods in the spectroscopic toolbox, such as standard population synthesis techniques and Lick indices. When trained and tested on 64x64-pixel images from the Sloan Digital Sky Survey, the global bimodality of star-forming and quiescent galaxies in photometric space is recovered, as well as a mass-metallicity relation of star-forming galaxies. The comparison between the observed and the artificially created spectra shows good agreement in overall metallicity, age, Dn4000, stellar velocity dispersion, and E(B-V) values. Photometric redshift estimates of our generative algorithm can compete with other current, specialized deep-learning techniques. Moreover, this work is the first attempt in the literature to infer velocity dispersion from photometric images. Additionally, we can predict the presence of an active galactic nucleus up to an accuracy of 82%. With our method, scientifically interesting galaxy properties, normally requiring spectroscopic inputs, can be obtained in future data sets from large-scale photometric surveys alone. The spectra prediction via AI can further assist in creating realistic mock catalogs.