The Strong Gravitational Lens Finding Challenge

R. Benton Metcalf , M. Meneghetti , Camille Avestruz , Fabio Bellagamba , Clécio R. Bom , Emmanuel Bertin (1) , Rémi Cabanac (2) , F. Courbin , Andrew Davies , Etienne Decencière (3) , Rémi Flamary (4) , Raphael Gavazzi (1) , Mario Geiger , Philippa Hartley (5) , Marc Huertas-Company , Neal Jackson (5) , C. Jacobs , Eric Jullo (6) , Jean-Paul Kneib , Léon V.E. Koopmans , François Lanusse , Chun-Liang Li , Quanbin Ma , Martin Makler , Nan Li , Matthew Lightman , Carlo Enrico Petrillo , Stephen Serjeant , Christoph Schäfer , Alessandro Sonnenfeld , Amit Tagore (5) , Crescenzo Tortora , Diego Tuccillo (7, 3) , Manuel B. Valentín , Santiago Velasco-Forero (3) , Gijs A. Verdoes Kleijn , Georgios Vernardos

1 IAP - Institut d'Astrophysique de Paris
2 IRAP - Institut de recherche en astrophysique et planétologie
3 CMM - Centre de Morphologie Mathématique
4 LAGRANGE - Joseph Louis LAGRANGE
5 OCA - Observatoire de la Côte d'Azur
6 LAM - Laboratoire d'Astrophysique de Marseille
7 LERMA - Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique et Atmosphères = Laboratory for Studies of Radiation and Matter in Astrophysics and Atmospheres

Large-scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images, and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects, reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100 000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. Having multi-band, ground based data is found to be better for this purpose than single-band space based data with lower noise and higher resolution, suggesting that multi-colour data is crucial. Multi-band space based data will be superior to ground based data. The most difficult challenge for a lens finder is differentiating between rare, irregular and ring-like face-on galaxies and true gravitational lenses. The degree to which the efficiency and biases of lens finders can be quantified largely depends on the realism of the simulated data on which the finders are trained.Key words: gravitational lensing: strong / methods: data analysis⋆ Provost’s Postdoctoral Scholar at the University of Chicago.

Mots clés: gravitational lensing: strong; methods: data analysis

Domaines: Astrophysique [astro-ph]; Instrumentations et Détecteurs [physics.ins-det]

R. Benton Metcalf, M. Meneghetti, Camille Avestruz, Fabio Bellagamba, Clécio R. Bom, et al.. The Strong Gravitational Lens Finding Challenge. Astronomy and Astrophysics - A&A, 2019, 625, pp.A119. ⟨10.1051/0004-6361/201832797⟩. ⟨hal-01737876⟩