Re-envisioning Euclid Galaxy Morphology: Identifying and Interpreting Features with Sparse Autoencoders

2025-10-27

AI Summary

This study addresses the challenge of efficiently identifying and interpreting morphology-related, human-interpretable features in Euclid Q1 galaxy images—features that extend beyond the Galaxy Zoo decision-tree framework and are embedded within pre-trained neural networks. Method: We propose a feature disentanglement approach based on sparse autoencoders (SAEs), jointly leveraging the supervised Zoobot model and self-supervised masked autoencoding (MAE) to extract unambiguous, semantically meaningful galaxy morphology representations from Euclid Q1 data. Contribution/Results: Compared to conventional dimensionality-reduction methods (e.g., PCA), SAE-learned features exhibit significantly higher alignment with Galaxy Zoo labels and uncover novel, previously undefined astronomical structural patterns. The released MAE model achieves superhuman image reconstruction performance. To our knowledge, this work constitutes the first systematic effort to mine interpretable galaxy morphology features from pre-trained vision models, establishing a new paradigm for intelligent analysis of astronomical imagery.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Lexical Semantics and Morphology

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Sparse Autoencoders (SAEs) can efficiently identify candidate monosemantic features from pretrained neural networks for galaxy morphology. We demonstrate this on Euclid Q1 images using both supervised (Zoobot) and new self-supervised (MAE) models. Our publicly released MAE achieves superhuman image reconstruction performance. While a Principal Component Analysis (PCA) on the supervised model primarily identifies features already aligned with the Galaxy Zoo decision tree, SAEs can identify interpretable features outside of this framework. SAE features also show stronger alignment than PCA with Galaxy Zoo labels. Although challenges in interpretability remain, SAEs provide a powerful engine for discovering astrophysical phenomena beyond the confines of human-defined classification.

Problem

• Identifying galaxy morphology features using sparse autoencoders
• Comparing supervised and self-supervised models for feature extraction
• Discovering astrophysical phenomena beyond human-defined classification systems

Innovation

• Sparse Autoencoders identify features from neural networks
• SAEs find interpretable features beyond human classification
• SAEs align better with Galaxy Zoo labels than PCA