The Strong Gravitational Lens Finding Challenge

• Published in: arXiv.org
• Main Authors: R Benton Metcalf, Meneghetti, M, Avestruz, Camille, Bellagamba, Fabio, Bom, Clécio R, Bertin, Emmanuel, Cabanac, Rémi, Courbin, F, Davies, Andrew, Decencière, Etienne, Flamary, Rémi, Gavazzi, Raphael, Geiger, Mario, Hartley, Philippa, Huertas-Company, Marc, Jackson, Neal, Jullo, Eric, Jean-Paul Kneib, Koopmans, Léon V E, Lanusse, François, Chun-Liang, Li, Ma, Quanbin, Makler, Martin, Li, Nan, Lightman, Matthew, Petrillo, Carlo Enrico, Serjeant, Stephen, Schäfer, Christoph, Sonnenfeld, Alessandro, Tagore, Amit, Tortora, Crescenzo, Tuccillo, Diego, Valentín, Manuel B, Velasco-Forero, Santiago, Gijs A Verdoes Kleijn, Vernardos, Georgios
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 20.03.2019
• Subjects: Artificial neural networks; Automation; Galaxies; Gravitation; Gravitational lenses; Identification methods; Image contrast; Physics - Astrophysics of Galaxies; Physics - Cosmology and Nongalactic Astrophysics; Physics - Instrumentation and Methods for Astrophysics; Support vector machines; Test procedures
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1802.03609

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. (abridged)