The promise of self-supervised and active learning for Strong Lens discovery: Astronomaly applied to KiDS

arXiv:2609.00154 · astro-ph.IM, astro-ph.GA · Submitted 2026-08-31 · Read on arXiv

astro-ph.IM, astro-ph.GA

Submitted: 2026-08-31

Updated: 2026-09-03

Comments: Accepted for publication in Monthly Notices of the Royal Astronomical Society (MNRAS)

Code: https://github.com/MichelleLochner/astronomalyhttps:

Project page: https://kids.strw.leidenuniv.nl/DR4

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Active Learning — 39× in this paper · explained in 6 episodes
• Feature Extraction — 12× in this paper · explained in 2 episodes
• Domain Shift — 7× in this paper · explained in 4 episodes
• AnomalyMatch — 6× in this paper · explained in 1 episode
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Gravitational Lensing — 5× in this paper · explained in 2 episodes
• Human-in-the-Loop — 5× in this paper · explained in 3 episodes
• Self-Supervised Learning — 5× in this paper · explained in 8 episodes
• Bootstrap Your Own Latent — 4× in this paper
• Clustering — 4× in this paper · explained in 2 episodes
• Downstream Tasks — 4× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Dark Energy Spectroscopic Instrument — 3× in this paper
• Representation Learning — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 2× in this paper
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Aperture — 1× in this paper · explained in 1 episode
• Astronomy Observatory and the National Research Foundation — 1× in this paper
• Computational Biology — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Mask Generation — 1× in this paper · explained in 2 episodes
• Nearest Neighbor — 1× in this paper
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Self-Supervised Learning (SSL) — 1× in this paper · explained in 4 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• Time-Delay Cosmography: Measuring the Hubble Constant and other cosmological parameters with strong gravitational lensing
• astromorph: Self-supervised machine learning pipeline for astronomical morphology analysis
• On the Opportunities and Risks of Foundation Models
• HOLISMOKES -- XI. Evaluation of supervised neural networks for strong-lens searches in ground-based imaging surveys
• Exploring Simple Siamese Representation Learning
• Euclid Quick Data Release (Q1): The Strong Lensing Discovery Engine A -- System overview and lens catalogue
• Euclid Quick Data Release (Q1). AstroVink: A vision transformer approach to find strong gravitational lens systems
• A targeted machine learning approach for detecting diffuse radio emission with Astronomaly: Protege
• Discovering Strong Gravitational Lenses in the Dark Energy Survey with Interactive Machine Learning and Crowd-sourced Inspection with Space Warps
• Does Machine Learning Work? A Comparative Analysis of Strong Gravitational Lens Searches in the Dark Energy Survey
• TEGLIE: Transformer encoders as strong gravitational lens finders in KiDS
• Bootstrap your own latent: A new approach to self-supervised Learning
• A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends
• AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning
• Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI) $-$ X. Strong Lens Finding in The HSC-SSP using Convolutional Neural Networks
• Euclid Definition Study Report
• The Dark Energy Spectroscopic Instrument (DESI)
• Astronomaly Protege: Discovery Through Human-Machine Collaboration
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Euclid: Finding strong gravitational lenses in the Early Release Observations using convolutional neural networks

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