Data characterization using artificial-star tests: performance evaluation

TL;DR

This paper introduces a new, computationally efficient method for artificial-star tests in crowded stellar fields by adding artificial stars to data catalogs rather than raw images, validated on the NGC 1818 cluster.

Contribution

The paper presents a novel approach for artificial-star tests that reduces computational time while maintaining accuracy, applicable to data with stable point-spread functions.

Findings

The new method produces equivalent results to traditional tests.

It significantly reduces computational time.

Validated on the NGC 1818 cluster.

Abstract

Traditional artificial-star tests are widely applied to photometry in crowded stellar fields. However, to obtain reliable binary fractions (and their uncertainties) of remote, dense, and rich star clusters, one needs to recover huge numbers of artificial stars. Hence, this will consume much computation time for data reduction of the images to which the artificial stars must be added. In this paper, we present a new method applicable to data sets characterized by stable, well-defined point-spread functions, in which we add artificial stars to the retrieved-data catalog instead of the raw images. Taking the young Large Magellanic Cloud cluster NGC 1818 as an example, we compare results from both methods and show that they are equivalent, while our new method saves significant computational time.