A method to search for strong galaxy-galaxy lenses in optical imaging surveys

Abstract

We present a semi-automated method to search for strong galaxy-galaxy lenses in optical imaging surveys. Our search technique constrains the shape of strongly lensed galaxies (or arcs) in a multi-parameter space, which includes the third order (octopole) moments of objects. This method is applied to the Deep Lens Survey (DLS), a deep ground based weak lensing survey imaging to $R\sim 26$. The parameter space of arcs in the DLS is simulated using real galaxies extracted from deep HST fields in order to more accurately reproduce the properties of arcs. Arcs are detected in the DLS using a pixel thresholding method and candidate arcs are selected within this multi-parameter space. Examples of strong galaxy-galaxy lens candidates discovered in the DLS F2 field (4 square degrees) are presented.

Accepted for publication in MNRAS, 12 pages

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• A Neural Network Gravitational Arc Finder based on the Mediatrix filamentation Method
• Lensing Probabilities for Spectroscopically Selected Galaxy-Galaxy Strong Lenses
• The DES Bright Arcs Survey: Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey 5,000 Sq. Deg. Footprint
• Deep Learning in Wide-field Surveys: Fast Analysis of Strong Lenses in Ground-based Cosmic Experiments
• Developing a Victorious Strategy to the Second Strong Gravitational Lensing Data Challenge
• Optimizing machine learning methods to discover strong gravitational lenses in the Deep Lens Survey