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Register a new userMachine learning and Kolmogorov analysis to reveal gravitational lenses
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Added by
Sergey Mirzoyan Dr.
Publication date
2019
fields
Physics
and research's language is
English
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We present an automated approach to detect and extract information from the astronomical datasets on the shapes of such objects as galaxies, star clusters and, especially, elongated ones such as the gravitational lenses. First, the Kolmogorov stochasticity parameter is used to retrieve the sub-regions that worth further attention. Then we turn to image processing and machine learning Principal Component Analysis algorithm to retrieve the sought objects and reveal the information on their morphologies. We show the capability of our automated method to identify distinct objects, including of and to classify them based on the input parameters. A catalog of possible lensing objects is retrieved as an output of the software, then their inspection is performed for the candidates that survive the filters applied.
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We consider a machine learning algorithm to detect and identify strong gravitational lenses on sky images. First, we simulate different artificial but very close to reality images of galaxies, stars and strong lenses, using six different methods, i.e. two for each class. Then we deploy a convolutional neural network architecture to classify these simulated images. We show that after neural network training process one achieves about 93 percent accuracy. As a simple test for the efficiency of the convolutional neural network, we apply it on an real Einstein cross image. Deployed neural network classifies it as gravitational lens, thus opening a way for variety of lens search applications of the deployed machine learning scheme.
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