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

TL;DR

astromorph is a user-friendly, self-supervised machine learning pipeline that extracts meaningful morphological features from large, complex astronomical datasets without manual labeling.

Contribution

It introduces a versatile, scalable framework based on BYOL that handles diverse data types and demonstrates broad applicability across different astronomical imaging datasets.

Findings

Produces scientifically meaningful embeddings capturing morphological differences

Successfully applied to protoplanetary disks and infrared dark clouds datasets

Enables label-free exploration of morphological patterns in astronomy

Abstract

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. We aim to provide a user-friendly implementation of a self-supervised machine learning framework to explore morphological properties of large datasets, based on the BYOL (Bootstrap Your Own Latents) 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. 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, offering streamlined pipeline scripts for ease of use as well as deeper customization options via PyTorch-based classes. To demonstrate the utility of astromorph, we apply it in two contrasting science cases representing different astronomical domains: images of protoplanetary disks observed with 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. 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 suggest that the pipeline is broadly applicable across a wide range of imaging-rich astronomical context, providing a user friendly tool for advancing discovery in observational astronomy.