astromorph: self-supervised machine learning pipeline for astronomical morphology analysis

¹ Department of Physics and Astronomy, Chalmers University of Technology, 412 96 Gothenburg, Sweden
² Department of Physics and Astronomy, Chalmers University of Technology, Onsala Space Observatory, 439 92 Onsala, Sweden
³ Chalmers e-Commons, Chalmers University of Technology, 412 96 Gothenburg, Sweden

^★ Corresponding authors: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 10 October 2025
Accepted: 6 February 2026

Abstract

Context. Modern telescopes generate increasingly large and diverse datasets, often consisting of complex and morphologically rich structures. To efficiently explore such data requires automated methods that can extract and organize physically meaningful information, ideally without the need for extensive manual interaction.

Aims. Our aim is to provide a user-friendly implementation of a self-supervised machine learning framework to explore morphological properties of large datasets, based on the Bootstrap Your Own Latents (BYOL) method. By enabling the generation of meaningful image embeddings without manually labelled data, the framework will enable key tasks such as clustering, anomaly detection, and similarity-based exploration.

Methods. We present astromorph, a Python package that implements the BYOL method in a way tailored for astronomical imaging. In contrast to existing BYOL implementations, astromorph accommodates data of varying dimensions and resolutions, including both single-channel FITS images and multi-channel spectral cubes. The package is built with usability in mind, and offers streamlined pipeline scripts for ease of use as well as deeper customization options via PyTorch-based classes.

Results. To demonstrate the utility of astromorph, we apply it in two contrasting science cases representing different astronomical domains: images of protoplanetary disks observed with the Atacama Large Millimeter/submillimeter Array (ALMA), and infrared dark clouds observed with Spitzer and Herschel. In both cases we demonstrate how astromorph produces scientifically meaningful embeddings that capture morphological differences and similarities across large samples.

Conclusions. astromorph enables users to apply a robust, label-free approach for uncovering morphological patterns in astronomical datasets. The successful application to two markedly different datasets suggests that the pipeline is broadly applicable across a wide range of imaging-rich astronomical contexts, providing a user-friendly tool for advancing discovery in observational astronomy.