Rare Galaxy Classes Identified In Foundation Model Representations

Abstract

We identify rare and visually distinctive galaxy populations by searching for structure within the learned representations of pretrained models. We show that these representations arrange galaxies by appearance in patterns beyond those needed to predict the pretraining labels. We design a clustering approach to isolate specific local patterns, revealing groups of galaxies with rare and scientifically-interesting morphologies.

Accepted at Machine Learning and the Physical Sciences Workshop, NeurIPS 2023

Cited by in corpus (1)

• Radio Galaxy Zoo: Morphological classification by Fanaroff-Riley designation using self-supervised pre-training