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Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine C: Finding lenses with machine learning
Article de revue scientifique   Open Access

Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine C: Finding lenses with machine learning

N.E.P Lines, T.E Collett, M Walmsley, K Rojas, T Li, L Leuzzi, A Manjón-García, S.H Vincken, J Wilde, P Holloway, …
Astron.Astrophys
2026
DOI: https://doi.org/10.1051/0004-6361/202554542

Résumé

Strong gravitational lensing has the potential to provide a powerful probe of astrophysics and cosmology, but fewer than 1000 strong lenses have been confirmed so far. With a resolution covering a third of the sky, the Euclid telescope will revolutionise the identification of strong lenses, with lenses forecasted to be discovered amongst the 1.5 billion galaxies it will observe. We present an analysis of the performance of five machine-learning models at finding strong gravitational lenses in the quick release of Euclid data (Q1) covering 63,deg^2. The models have been validated by citizen scientists and expert visual inspection. We focus on the best-performing network: a fine-tuned version of the Zoobot pretrained model originally trained to classify galaxy morphologies in heterogeneous astronomical imaging surveys. Of the one million Q1 objects that Zoobot was tasked to find strong lenses within, the top 1000 ranked objects contain 122 grade A lenses (almost-certain lenses) and 41 grade B lenses (probable lenses). A deeper search with the five networks combined with visual inspection yielded 250 (247) grade A (B) lenses, of which 224 (182) are ranked in the top by Zoobot . When extrapolated to the full Euclid survey, the highest ranked one million images will contain grade A or B strong gravitational lenses.

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