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

Yi Yang, Zhenzhen Shao, Xiaofeng Wang, Xiaochen Zheng

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.

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

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 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.
Paper Structure (13 sections, 5 figures)

This paper contains 13 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Data
  3. Method and Data Processing
  4. Filtering and crossmatching
  5. Data preprocessing
  6. Curve fitting and TRGB identification
  7. Results and Discussion
  8. TRGB Results
  9. Discussion
  10. Relationship between TRGB absolute magnitude and metallicity
  11. Relationship between TRGB color index and metallicity
  12. Performance and Applications of the Automated Method
  13. Conclusions

Figures (5)

  • Figure 1: Diagram of the TRGB seeking procedure, which includes data filtering and cross-referencing, data preprocessing, and curve fitting and TRGB finding.
  • Figure 2: Color-magnitude diagram for NGC 5139, showing sources with memberprob greater than 99% (blue dots), sources with memberprob in range from 90% to 99%, polynomial fit (red curve), outliers removed by LOF (green dots), and the identified TRGB candidate (magenta triangle).
  • Figure 3: Absolute magnitudes vs metallicity of TRGBs, showing TRGB samples identified consistently by both machine recognition and manual selection (green dots), inconsistent samples (red dots), and newly added star clusters (black triangles).
  • Figure 4: Absolute magnitudes vs metallicity of TRGBs. Left panel: The blue dots represent data from selected samples, gray dots represent closest neighbors from each cluster's selected sample, blue and gray lines denote the fitting curves repectively; Right panel: Same as left panel with MI averaged in [Fe/H] bins of 0.3 dex intervals.
  • Figure 5: Relationship between metallicity ([Fe/H]) and color index (V $-$ I) of TRGB stars, with the fitted curve representing the trend. Red x markers denote samples in 1990-DaCosta-Standard-A, and black dots denote our samples.