  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.
