Astroinformatics based search for globular clusters in the Fornax Deep Survey

TL;DR

This paper demonstrates an Astroinformatics approach using machine learning and feature selection to identify globular clusters in the Fornax galaxy cluster, achieving results comparable to traditional methods.

Contribution

It introduces a multidisciplinary methodology combining neural-gas clustering and a novel feature selection technique for globular cluster detection in deep survey data.

Findings

Achieves comparable purity and completeness to HST-based methods.

Demonstrates effectiveness of machine learning in astrophysical object detection.

Validates Astroinformatics as a powerful tool for galaxy cluster studies.

Abstract

In the last years, Astroinformatics has become a well defined paradigm for many fields of Astronomy. In this work we demonstrate the potential of a multidisciplinary approach to identify globular clusters (GCs) in the Fornax cluster of galaxies taking advantage of multi-band photometry produced by the VLT Survey Telescope using automatic self-adaptive methodologies. The data analyzed in this work consist of deep, multi-band, partially overlapping images centered on the core of the Fornax cluster. In this work we use a Neural-Gas model, a pure clustering machine learning methodology, to approach the GC detection, while a novel feature selection method ($\Phi$LAB) is exploited to perform the parameter space analysis and optimization. We demonstrate that the use of an Astroinformatics based methodology is able to provide GC samples that are comparable, in terms of purity and completeness with those obtained using single band HST data (Brescia et al. 2012) and two approaches based respectively on a morpho-photometric (Cantiello et al. 2018b) and a PCA analysis (D'Abrusco et al. 2015) using the same data discussed in this work.