Speaker
Description
In the era of big-data astronomy, automated and scalable galaxy classification is essential to handle the output of next-generation surveys. This talk presents an application of AstroCLIP, a state-of-the-art cross-modal foundation model, to classify 423 spectroscopically confirmed member galaxies of the cluster Abell S1063 from the CLASH-VLT survey. Leveraging the Transformer architecture and self-supervised contrastive learning, AstroCLIP embeds spectroscopic and photometric data into a shared latent space, allowing the downstream classifier to extract complementary physical information, thus outperforming a single-modal approach. The results highlight the power of foundation models to overcome labeled data scarcity through robust data representations and transfer learning.