Neural Network Astronomy as a New Tool for Observing Bright and Compact Objects

Abstract

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.

15 pages, 6 figures

References in corpus (3)

• RadioAstron -- a Telescope with a Size of 300 000 km: Main Parameters and First Observational Results
• Imaging the Supermassive Black Hole Shadow and Jet Base of M87 with the Event Horizon Telescope
• Model Predictions of the Results of Interferometric Observations for Stars under Conditions of Strong Gravitational Scattering by Black Holes and Wormholes

Cited by in corpus (1)

• Deep learning inference with the Event Horizon Telescope II. The Zingularity framework for Bayesian artificial neural networks