# Machine learning and Kolmogorov analysis to reveal gravitational lenses

## TL;DR

This paper introduces an automated method combining Kolmogorov stochasticity and machine learning PCA to detect, classify, and catalog gravitational lenses and other astronomical objects in large datasets.

## Contribution

The novel approach integrates Kolmogorov analysis with PCA for efficient detection and classification of gravitational lenses in astronomical data.

## Key findings

- Successfully identified and classified gravitational lenses
- Generated a catalog of potential lensing objects
- Demonstrated high accuracy in object detection and classification

## Abstract

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.