A neural network gravitational arc finder based on the Mediatrix filamentation method

¹ Centro Brasileiro de Pesquisas Físicas, Rua Dr. Xavier Sigaud 150, 22290-180, Rio de Janeiro RJ, Brazil
e-mail: clecio@debom.com.br
² Centro Federal de Educação Tecnológica Celso Suckow da Fonseca, Rodovia Mário Covas, lote J2, quadra J, 23810-000 Itaguaí, RJ, Brazil
³ International Center for Relativistic Astrophysics Network (ICRANet), University of Rome “La Sapienza”, P.le Aldo Moro, 5, 00185 Roma, Italy
⁴ ASI, via del Politecnico snc, 00133 Roma, Italy
⁵ CAPES Foundation, Ministry of Education of Brazil, 70040-020 Brasilia, DF, Brazil

Received: 21 June 2016
Accepted: 11 August 2016

Abstract

Context. Automated arc detection methods are needed to scan the ongoing and next-generation wide-field imaging surveys, which are expected to contain thousands of strong lensing systems. Arc finders are also required for a quantitative comparison between predictions and observations of arc abundance. Several algorithms have been proposed to this end, but machine learning methods have remained as a relatively unexplored step in the arc finding process.

Aims. In this work we introduce a new arc finder based on pattern recognition, which uses a set of morphological measurements that are derived from the Mediatrix filamentation method as entries to an artificial neural network (ANN). We show a full example of the application of the arc finder, first training and validating the ANN on simulated arcs and then applying the code on four Hubble Space Telescope (HST) images of strong lensing systems.

Methods. The simulated arcs use simple prescriptions for the lens and the source, while mimicking HST observational conditions. We also consider a sample of objects from HST images with no arcs in the training of the ANN classification. We use the training and validation process to determine a suitable set of ANN configurations, including the combination of inputs from the Mediatrix method, so as to maximize the completeness while keeping the false positives low.

Results. In the simulations the method was able to achieve a completeness of about 90% with respect to the arcs that are input into the ANN after a preselection. However, this completeness drops to ~ 70% on the HST images. The false detections are on the order of 3% of the objects detected in these images.

Conclusions. The combination of Mediatrix measurements with an ANN is a promising tool for the pattern-recognition phase of arc finding. More realistic simulations and a larger set of real systems are needed for a better training and assessment of the efficiency of the method.