The strong gravitational lens finding challenge

¹ Dipartimento di Fisica & Astronomia, Università di Bologna, Via Gobetti 93/2, 40129 Bologna, Italy
e-mail: robertbenton.metcalf@unibo.it
² INAF-Osservatorio Astronomico di Bologna, Via Ranzani 1, 40127 Bologna, Italy
³ Enrico Fermi Institute, The University of Chicago, Chicago, IL 60637, USA
⁴ Kavli Institute for Cosmological Physics, The University of Chicago, Chicago, IL 60637, USA
⁵ Department of Astronomy & Astrophysics, The University of Chicago, Chicago, IL 60637, USA
⁶ Centro Federal de Educação Tecnológica Celso Suckow da Fonseca, 23810-000 Itaguaí, RJ, Brazil
⁷ Centro Brasileiro de Pesquisas Físicas, 22290-180 Rio de Janeiro, RJ, Brazil
⁸ Institut d’Astrophysique de Paris, Sorbonne Université, CNRS, UMR 7095, 98 bis bd Arago, 75014 Paris, France
⁹ IRAP, Université de Toulouse, CNRS, UPS, Toulouse, France
¹⁰ MINES Paristech, PSL Research University, Centre for Mathematical Morphology, 35 rue Saint-Honoré, Fontainebleau, France
¹¹ Laboratoire Lagrange, Universié de Nice Sophia-Antipolis, Centre National de la Recherche Scientifique, France
¹² Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland
¹³ Jodrell Bank Centre for Astrophysics, School of Physics & Astronomy, University of Manchester, Oxford Rd, Manchester M13 9PL, UK
¹⁴ Observatoire de la Côte d’Azur, Parc Valrose, 06108 Nice, France
¹⁵ LERMA, Observatoire de Paris, CNRS, Université Paris Diderot, 61, Avenue de l’Observatoire, 75014 Paris, France
¹⁶ Aix Marseille Université, CNRS, LAM (Laboratoire d’Astrophysique de Marseille) UMR 7326, 13388 Marseille, France
¹⁷ Kapteyn Astronomical Institute, University of Groningen, Postbus 800, 9700 AV Groningen, The Netherlands
¹⁸ McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
¹⁹ School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
²⁰ School of Physics and Astronomy, Nottingham University, University Park, Nottingham NG7 2RD, UK
²¹ JPMorgan Chase, Chicago, IL 60603, USA
²² Kavli IPMU (WPI), UTIAS, The University of Tokyo, Kashiwa, Chiba 277-8583, Japan
²³ School of Physical Sciences, The Open University, Walton Hall, Milton Keynes MK7 6AA, UK
²⁴ Centre for Astrophysics and Supercomputing, Swinburne University of Technology, PO Box 218 Hawthorn, VIC 3122, Australia

Received: 8 February 2018
Accepted: 13 March 2019

Abstract

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.