Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey
Xiaosheng Huang, Christopher Storfer, Vikram Ravi, Andrew Pilon, M. Domingo, David J. Schlegel, S. Bailey, Arjun Dey, R. Gupta, David Herrera, S. Juneau, Martin Landriau, Dustin Lang, Aaron Meisner, John Moustakas, Adam D. Myers, Edward F. Schlafly, F. Valdés, B. A. Weaver, Jinyi Yang, Christophe Yèche
Abstract
Abstract We perform a semi-automated search for strong gravitational lensing systems in the 9000 deg 2 Dark Energy Camera Legacy Survey (DECaLS), part of the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys. The combination of the depth and breadth of these surveys are unparalleled at this time, making them particularly suitable for discovering new strong gravitational lensing systems. We adopt the deep residual neural network architecture developed by Lanusse et al. for the purpose of finding strong lenses in photometric surveys. We compile a training sample that consists of known lensing systems in the Legacy Surveys and the Dark Energy Survey as well as non-lenses in the footprint of DECaLS. In this paper we show the results of applying our trained neural network to the cutout images centered on galaxies typed as ellipticals in DECaLS. The images that receive the highest scores (probabilities) are visually inspected and ranked. Here we present 335 candidate strong lensing systems, identified for the first time.