A simple prescription for simulating and characterizing gravitational arcs

¹ Departamento de AstronomiaUniversidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500, RS, 91501-970 Porto Alegre, Brazil
e-mail: furlanetto.cristina@gmail.com
² Laboratório Interinstitucional de e-Astronomia, Rua Gen. José Cristino, 77, RJ, 20921-400 Rio de Janeiro, Brazil
³ Centro Brasileiro de Pesquisas Físicas, Rua Dr. Xavier Sigaud 150, RJ, 22290-180 Rio de Janeiro, Brazil
⁴ Laboratório Nacional de Computação Científica, Av. Getúlio Vargas, 333, RJ, 25651-075 Petrópolis, Brazil
⁵ Departamento de Física, Universidade Federal do Rio Grande do Norte, Campus Universitário, RN, 59072-970 Natal, Brazil
⁶ Observatório Nacional, Rua Gen. José Cristino, 77, RJ 20921-400 Rio de Janeiro, Brazil

Received: 14 September 2012
Accepted: 5 November 2012

Abstract

Simple models of gravitational arcs are crucial for simulating large samples of these objects with full control of the input parameters. These models also provide approximate and automated estimates of the shape and structure of the arcs, which are necessary for detecting and characterizing these objects on massive wide-area imaging surveys. We here present and explore the ArcEllipse, a simple prescription for creating objects with a shape similar to gravitational arcs. We also present PaintArcs, which is a code that couples this geometrical form with a brightness distribution and adds the resulting object to images. Finally, we introduce ArcFitting, which is a tool that fits ArcEllipses to images of real gravitational arcs. We validate this fitting technique using simulated arcs and apply it to CFHTLS and HST images of tangential arcs around clusters of galaxies. Our simple ArcEllipse model for the arc, associated to a Sérsic profile for the source, recovers the total signal in real images typically within 10%−30%. The ArcEllipse+Sérsic models also automatically recover visual estimates of length-to-width ratios of real arcs. Residual maps between data and model images reveal the incidence of arc substructure. They may thus be used as a diagnostic for arcs formed by the merging of multiple images. The incidence of these substructures is the main factor that prevents ArcEllipse models from accurately describing real lensed systems.