A Neural Network Gravitational Arc Finder based on the Mediatrix filamentation Method

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. In this work we introduce a new arc finder based on pattern recognition, which uses a set of morphological measurements 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. 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. In the simulations the method was able to achieve a completeness of about 90% with respect to the arcs that are input to the ANN after a preselection. However, this completeness drops to $\sim$ 70% on the HST images. The false detections are of the order of 3% of the objects detected in these images. 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.Comment: Updated to match published versio

Detectability of Cosmic Topology in Generalized Chaplygin Gas Models

If the spatial section of the universe is multiply connected, repeated images or patterns are expected to be detected observationally. However, due to the finite distance to the last scattering surface, such pattern repetitions could be unobservable. This raises the question of whether a given cosmic topology is detectable, depending on the values of the parameters of the cosmological model. We study how detectability is affected by the choice of the model itself for the matter-energy content of the universe, focusing our attention on the generalized Chaplygin gas (GCG) model for dark matter and dark energy unification, and investigate how the detectability of cosmic topology depends on the GCG parameters. We determine to what extent a number of topologies are detectable for the current observational bounds on these parameters. It emerges from our results that the choice of GCG as an alternative to the $\Lambda$CDM matter-energy content model has an impact on the detectability of cosmic topology.Comment: Submitted to A&

Analytic Solutions for Navarro--Frenk--White Lens Models for Low Characteristic Convergences

The Navarro-Frenk-White (NFW) density profile is often used to model gravitational lenses. For low values of the characteristic convergence ($\kappa_s \ll 1$) of this model - corresponding to galaxy and galaxy group mass scales - a high numerical precision is required in order to accurately compute several quantities in the strong lensing regime. An alternative for fast and accurate computations is to derive analytic approximations in this limit. In this work we obtain analytic solutions for several lensing quantities for elliptical (ENFW) and pseudo-elliptical (PNFW) NFW lens models on the typical scales where gravitational arcs are expected to be formed, in the $\kappa_s \ll 1$ limit, establishing their domain of validity. We derive analytic solutions for the convergence and shear for these models, obtaining explicit expressions for the iso-convergence contours and constant distortion curves (including the tangential critical curve). We also compute the deformation cross section, which is given in closed form for the circular NFW model and in terms of a one-dimensional integral for the elliptical ones. In addition, we provide a simple expression for the ellipticity of the iso-convergence contours of the pseudo-elliptical models and the connection of characteristic convergences among the PNFW and ENFW models. We conclude that the set of solutions derived here is generally accurate for $\kappa_s \lesssim 0.1$. For low ellipticities, values up to $\kappa_s \simeq 0.18$ are allowed. On the other hand, the mapping between PNFW and the ENFW models is valid up to $\kappa_s \simeq 0.4$. The solutions derived in this work can be used to speed up numerical codes and ensure their accuracy in the low $\kappa_s$ regime, including applications to arc statistics and other strong lensing observables. (Abridged)Comment: Accepted for publication in A&

A Systematic Search for High Surface Brightness Giant Arcs in a Sloan Digital Sky Survey Cluster Sample

We present the results of a search for gravitationally-lensed giant arcs conducted on a sample of 825 SDSS galaxy clusters. Both a visual inspection of the images and an automated search were performed and no arcs were found. This result is used to set an upper limit on the arc probability per cluster. We present selection functions for our survey, in the form of arc detection efficiency curves plotted as functions of arc parameters, both for the visual inspection and the automated search. The selection function is such that we are sensitive only to long, high surface brightness arcs with g-band surface brightness mu_g 10. Our upper limits on the arc probability are compatible with previous arc searches. Lastly, we report on a serendipitous discovery of a giant arc in the SDSS data, known inside the SDSS Collaboration as Hall's arc.Comment: 34 pages,8 Fig. Accepted ApJ:Jan-200