Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models
<|reference_start|>Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models: 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.<|reference_end|>
@article{doorenbos2024galaxy,
    title={Galaxy spectroscopy without spectra: Galaxy properties from photometric
  images with conditional diffusion models},
    author={Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo
  Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo
  M'arquez-Neila},
    journal={arXiv preprint arXiv:2406.18175},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18175},
    primaryClass={astro-ph.GA astro-ph.IM cs.AI}
}