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

## TL;DR

This paper introduces a neural network-based method for analyzing high-resolution interferometric astronomical data, enabling automated recognition and classification of celestial objects with accuracy comparable to human experts.

## Contribution

It presents a novel neural network approach for processing interferometric data and demonstrates its effectiveness in recognizing celestial objects like Sgr A*.

## Key findings

- Neural network can classify celestial objects with accuracy comparable to humans.
- The method is effective on simulated images from the proposed model.
- Neural networks can be trained on probable images to recognize real astronomical 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.