A trade-off between speed and information controls our understanding of astronomical objects. Fast-to-acquire photometric observations provide global properties, while costly and time-consuming spectroscopic measurements enable a better understanding of the physics governing their evolution. Here, we tackle this problem by generating spectra directly from photometry, through which we obtain an estimate of their intricacies from easily acquired images. This is done by using multi-modal conditional diffusion models, where the best out of the generated spectra is selected with a contrastive network. Initial experiments on minimally processed SDSS galaxy data show promising results.
@article{arxiv.2211.05556,
title = {Generating astronomical spectra from photometry with conditional diffusion models},
author = {Lars Doorenbos and Stefano Cavuoti and Giuseppe Longo and Massimo Brescia and Raphael Sznitman and Pablo Márquez-Neila},
journal= {arXiv preprint arXiv:2211.05556},
year = {2022}
}
Comments
Accepted at NeurIPS 2022 Machine Learning and the Physical Sciences workshop