A Neural Network Gravitational Arc Finder based on the Mediatrix filamentation Method
C. R. Bom, M. Makler, M. P. Albuquerque, C. H. Brandt

TL;DR
This paper introduces a neural network-based arc finder using the Mediatrix filamentation method, achieving high completeness in simulated data and promising results on HST images for automated strong lensing arc detection.
Contribution
The paper presents a novel pattern recognition approach combining Mediatrix measurements with an artificial neural network for automated arc detection in astronomical images.
Findings
Achieved about 90% completeness on simulated arcs
Detected approximately 70% completeness on HST images
False positive rate around 3% in real images
Abstract
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…
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