Finding strong lenses in CFHTLS using convolutional neural networks
Sign Up to like & getrecommendations! Published in 2017 at "Monthly Notices of the Royal Astronomical Society"
DOI: 10.1093/mnras/stx1492
Abstract: We train and apply convolutional neural networks, a machine learning technique developed to learn from and classify image data, to Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the identification of potential strong lensing systems. An… read more here.
Keywords: strong lenses; neural networks; survey; finding strong ... See more keywords
Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS
Sign Up to like & getrecommendations! Published in 2018 at "Monthly Notices of the Royal Astronomical Society"
DOI: 10.1093/mnras/sty2683
Abstract: Convolutional Neural Networks (ConvNets) are one of the most promising methods for identifying strong gravitational lens candidates in survey data. We present two ConvNet lens-finders that we have trained with a dataset composed of real… read more here.
Keywords: strong gravitational; neural networks; convolutional neural; convnet ... See more keywords
Using deep Residual Networks to search for galaxy-Ly α emitter lens candidates based on spectroscopic selection
Sign Up to like & getrecommendations! Published in 2018 at "Monthly Notices of the Royal Astronomical Society"
DOI: 10.1093/mnras/sty2708
Abstract: More than one hundred galaxy-scale strong gravitational lens systems have been found by searching for the emission lines coming from galaxies with redshifts higher than the lens galaxies. Based on this spectroscopic-selection method, we introduce… read more here.
Keywords: based spectroscopic; residual networks; networks search; spectroscopic selection ... See more keywords