Automated Identification of the Tip of the Red Giant Branch in Globular Clusters with Gaia Data

TL;DR

This paper presents an automated method using Gaia data and algorithmic analysis to identify the tip of the red giant branch in globular clusters, improving efficiency and expanding sample size.

Contribution

The study introduces a novel automated approach combining Gaia data and LOF algorithm to identify TRGB in globular clusters, increasing sample size and accuracy.

Findings

Expanded TRGB sample to 91 clusters with new data

Confirmed decreasing I-band luminosity trend with higher metallicity

TRGB I-band magnitude is approximately -4.02 at low metallicity

Abstract

This study introduces an automated approach for identifying the tip of the red giant branch (TRGB) in globular clusters, combining astronomical data with algorithmic methods. Using a dataset of 160 globular clusters and Python scripts, we matched stellar sources with Gaia data. Our script generates color-magnitude diagrams (CMDs), and uses the local outlier factor (LOF) algorithm to remove outliers. Applying a second-degree polynomial to fit red giant branch (RGB), we identify the TRGB as the star closest to the fitted curve's endpoint. By this method, we expanded TRGB samples in global clusters to 91 with newer observational data. Our results show a decreasing trend in I-band luminosity for metallicities greater than $-$1, consistent with previous studies. The results show a robust trend fitting and the $\rm M_{I}$ of TRGB is about $-$4.02 with extremely low metallicity. Our approach enhances TRGB identification efficiency while providing valuable insights for developing automatic tools in astronomical data analysis.