Star Cluster Detection and Characterization using Generalized Parzen Density Estimation

TL;DR

This paper evaluates the Parzen Density Estimation method for detecting and characterizing star clusters, demonstrating its effectiveness over traditional star count methods, especially in high background density regions.

Contribution

The study introduces the use of Gaussian Parzen Windows for star cluster detection, optimizing parameters and showing improved detection of small clusters in complex backgrounds.

Findings

Successfully detects clusters in simulated and real star fields

Identifies optimal Gaussian kernel parameters for accurate estimates

Detects small clusters where traditional methods fail

Abstract

Star cluster studies hold the key to understanding star formation, stellar evolution, and origin of galaxies. The detection and characterization of clusters depend on the underlying background density and the cluster richness. We examine the ability of the Parzen Density Estimation (a.k.a. Parzen Windows) method, which is a generalization of the well-known Star Count method, to detect clusters and measure their properties. We apply it on a range of simulated and real star fields, considering square and circular windows, with and without Gaussian kernel smoothing. Our method successfully identifies clusters and we suggest an optimal standard deviation of the Gaussian Parzen window for obtaining the best estimates of these parameters. Finally, we demonstrate that the Parzen Windows with Gaussian kernels are able to detect small clusters in regions of relatively high background density where the Star Count method fails.