New algorithm for astrometric reduction of the wide-field images

TL;DR

This paper introduces a modified iterative algorithm for wide-field image astrometric reduction that automatically selects the best model, significantly reducing systematic errors caused by optical imperfections in large telescopes.

Contribution

The paper presents a novel algorithm combining OLS and Student t-criterion for automatic model selection in astrometric reduction of wide-field images.

Findings

Reduces systematic errors in astrometric measurements.

Automatically selects optimal reduction model.

Improves accuracy of wide-field astronomical imaging.

Abstract

In this paper we presented the modified algorithm for astrometric reduction of the wide-field images. This algorithm is based on the iterative using of the method of ordinary least squares (OLS) and statistical Student t-criterion. The proposed algorithm provides the automatic selection of the most probabilistic reduction model. This approach allows eliminating almost all systematic errors that are caused by imperfections in the optical system of modern large telescopes.