Researchers Created A Star-Galaxy Classification Framework Utilizing Neural Networks

When scientists discover a new star or galaxy, they typically rely on information gleaned from academic papers, catalogs and other existing information to classify their discovery. This may be typical, but it’s not as efficient as it could be. That’s why researchers from the University of Illinois have created a star-galaxy classification framework that utilizes neural networks for all the heavy lifting. The deep convolutional neural network, referred to as ConvNets by creators Edward J. Kim and Robert J. Brunner, fetches data direct from the Sloan Digital Sky Survey and Canada-France-Hawaii-Telescope Lensing Survey to compile accurate classifications that are competitive with more conventional machine learning techniques. This isn’t the first time neural networks have been utilized when dealing with astronomy, with artificial neural networks first applied with star-galaxy classification back…


Link to Full Article: Researchers Created A Star-Galaxy Classification Framework Utilizing Neural Networks