IdentYS: A Python-Based Tool for Identifying Young Stars in Star-Forming Regions

TL;DR

IdentYS is a Python tool that efficiently identifies young stellar objects in star-forming regions using infrared photometry and color-color diagrams, aiding large-scale studies of stellar populations.

Contribution

This paper introduces the IdentYS software, a novel Python-based tool that improves the identification of young stars with IR excess in embedded regions, enhancing analysis accuracy.

Findings

Successfully identifies YSOs with IR excess using multiple color-color diagrams.

Reduces contamination from field stars and extragalactic objects.

Facilitates large-scale statistical analysis of young stellar populations.

Abstract

Research on young stellar populations is essential to understand the properties of embedded clusters and advance theories of their formation. This has driven advancements in methodologies for star detection, leading to the development of valuable databases and software. We present the scientific justification and operating principles of the IdentYS tool, which is designed to identify young stellar objects (YSOs) in star-forming regions. The tool facilitates the identification of young stars with infrared (IR) excess in remote and embedded star-forming regions, focusing primarily on Class I and II YSOs. For this purpose, near- and mid-IR photometric data and five colour-colour diagrams (J - H) vs. (H - K), K - [3.6] vs. [3.6] - [4.5], [3.6] - [4.5] vs. [5.8] - [8.0], [3.6] - [4.5] vs. [8.0] - [24], and [3.4] - [4.6] vs. [4.6] - [12] are used. The purity of the YSOs sample is enhanced by excluding field contamination from stellar and extragalactic objects. As a result, we compile a list of YSO candidates displaying the source designation, astrometric, and photometric parameters, as well as information on the evolutionary stage determined by the presence of IR excess, as indicated by certain diagrams. The application of this program can greatly streamline the statistical analysis of young stellar populations across diverse star-forming regions, including distant and deeply embedded ones, which typically require processing large volumes of initial data.