In this work, we present our classification algorithm to identify strong gravitational lenses from wide-area surveys using machine learning convolutional neural network; LensExtractor. We train and test the algorithm using a wide variety of strong gravitational lens configurations from simulations of lensing events. Images are processed through multiple convolutional layers which extract feature maps necessary to assign a lens probability to each image. LensExtractor provides a ranking scheme for all sources which could be used to identify potential gravitational lens candidates significantly reducing the number of images that have to be visually inspected. We further apply our algorithm to the \textit{HST}/ACS i-band observations of the COSMOS field and present our sample of identified lensing candidates. The developed machine learning algorithm is much more computationally efficient than classical lens identification algorithms and is ideal for discovering such events across wide areas from current and future surveys such as LSST and WFIRST.
@article{arxiv.1705.05857,
title = {LensExtractor: A Convolutional Neural Network in Search of Strong Gravitational Lenses},
author = {Milad Pourrahmani and Hooshang Nayyeri and Asantha Cooray},
journal= {arXiv preprint arXiv:1705.05857},
year = {2018}
}