Neural Network Astronomy as a New Tool for Observing Bright and Compact Objects. (arXiv:1905.07407v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Shatskiy_A/0/1/0/all/0/1">Alexander Shatskiy</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Evgeniev_I/0/1/0/all/0/1">Ivan Evgeniev</a>
We propose a new method for solving an important problem of astronomy that
arises in observations with ultrahigh-angular-resolution interferometers. This
method is based on the application of the theory of artificial neural networks.
We propose and compute a multiparameter model for a celestial object like Sgr
A*. For this model we have numerically constructed a number of probable images
for neural network training. After neural network training on these images, the
quality of its operation has been tested on another series of images from the
same model. We have proven that a neural network can recognize and classify
celestial objects (also obtained from interferometers) virtually no worse than
can be done by a human.
We propose a new method for solving an important problem of astronomy that
arises in observations with ultrahigh-angular-resolution interferometers. This
method is based on the application of the theory of artificial neural networks.
We propose and compute a multiparameter model for a celestial object like Sgr
A*. For this model we have numerically constructed a number of probable images
for neural network training. After neural network training on these images, the
quality of its operation has been tested on another series of images from the
same model. We have proven that a neural network can recognize and classify
celestial objects (also obtained from interferometers) virtually no worse than
can be done by a human.
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