Authors: Subhamoy Chatterjee (SwRI), Andrés Muñoz-Jaramillo (SwRI), Anna Malanushenko (HAO)

Deep generative models have shown immense potential in generating unseen data that has properties of real data. These models learn complex data-generating distributions starting from a smaller set of latent dimensions. However, generative models has encountered great skepticism in scientific domains due to the disconnection between generative latent vectors and scientifically relevant quantities. In this study, we integrate three types of machine learning models to generate high-quality solar magnetic patches in a physically interpretable manner and use those as a query to find matching patches in real observations. We find that the retrieved real data that shares the same physical properties as the generated query. This elevates Generative AI from a means-to-produce artificial data to a novel tool for scientific data interrogation.