Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine E -- Ensemble classification of strong gravitational lenses: lessons for Data Release 1 · arXivDesk
2503.15328Mar 19, 2025Paper submitted as part of the A&A Special Issue `Euclid Quick Data Release (Q1)', 15 pages, 8 figures
Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine E -- Ensemble classification of strong gravitational lenses: lessons for Data Release 1
Euclid Collaboration, P. Holloway, A. Verma, M. Walmsley, P. J. Marshall, A. More, T. E. Collett, N. E. P. Lines +338
The Euclid Wide Survey (EWS) is expected to identify of order 100000 galaxy-galaxy strong lenses across 14000deg2. The Euclid Quick Data Release (Q1) of 63.1deg2
Nearby in the stack
Euclid images provides an excellent opportunity to test our lens-finding ability, and to verify the anticipated lens frequency in the EWS. Following the Q1 data release, eight machine learning networks from five teams were applied to approximately one million images. This was followed by a citizen science inspection of a subset of around
100000
images, of which
65%
received high network scores, with the remainder randomly selected. The top scoring outputs were inspected by experts to establish confident (grade A), likely (grade B), possible (grade C), and unlikely lenses. In this paper we combine the citizen science and machine learning classifiers into an ensemble, demonstrating that a combined approach can produce a purer and more complete sample than the original individual classifiers. Using the expert-graded subset as ground truth, we find that this ensemble can provide a purity of
52±2%
(grade A/B lenses) with
50%
completeness (for context, due to the rarity of lenses a random classifier would have a purity of
0.05%
). We discuss future lessons for the first major Euclid data release (DR1), where the big-data challenges will become more significant and will require analysing more than
∼300
million galaxies, and thus time investment of both experts and citizens must be carefully managed.