Evaluating Zero-Shot Anomaly Detection for Identifying Unusual Galaxy Morphologies in CEERS and NGDEEP

Abstract

Galaxy morphology out to $z\sim 6$ provides insight into galaxy formation and evolution, but identifying rare or structurally complex systems is difficult due to observational limits and the scale of modern imaging surveys. Here, we apply AdaCLIP, a Zero-Shot Anomaly Detection (ZSAD) model built upon the Contrastive Language-Image Pretraining (CLIP) model and adapted using non-astronomical anomaly-detection datasets, to identify structurally unusual galaxies without galaxy-specific anomaly-detection training or morphological labels. We extract anomaly scores and 768-dimensional feature embeddings from 38,853 galaxies in the Cosmic Evolution Early Release Science (CEERS) and Next Generation Deep Extragalactic Exploratory Public (NGDEEP) surveys, and compare the resulting anomaly scores with parametric GALFIT and nonparametric statmorph measurements. The feature embeddings reveal structure associated with galaxy morphology and observational properties, while anomaly scores show modest trends with structural measures, including asymmetry, concentration, $M_{20}$, and size, but weaker or less consistent trends with Sérsic index, axis ratio, smoothness, and Gini coefficient. These results highlight the potential of ZSAD as a scalable tool for identifying unusual galaxy morphologies in large astronomical imaging surveys, including those anticipated from the Nancy Grace Roman Space Telescope.