Euclid quick data release (Q1)

Abstract

The Euclid Wide Survey (EWS) is expected to identify in the order of 100 000 galaxy-galaxy strong lenses across 14 000deg 2 . The Euclid Quick Data Release (Q1) of 63.1deg 2 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 100 000 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% and the best machine learning network in this work achieved 7.3% purity for the same completeness). 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 the time investment of both experts and citizens must be carefully managed.