Locating Red Supergiants in the Galaxy NGC 6822

TL;DR

This study identifies and characterizes red supergiants in NGC 6822 using near-IR data, compares their properties with stellar evolution models, and highlights the importance of binary interactions in their evolution.

Contribution

It provides a new catalog of RSGs in NGC 6822, applies a multi-step filtering process, and demonstrates that binary evolution models best fit the observed properties.

Findings

BPASS models with binary evolution best fit the data

Presence of a group of cool RSGs indicating binary interactions

Radial velocity alone is insufficient for membership determination

Abstract

Using archival near-IR photometry, we identify 51 of the K-band brightest red supergiants (RSGs) in NGC 6822 and compare their physical properties with stellar evolutionary model predictions. We first use Gaia parallax and proper motion values to filter out foreground Galactic red dwarfs before constructing a J - K vs. K color-magnitude diagram to eliminate lower-mass asymptotic giant branch star contaminants in NGC 6822. We then cross-match our results to previously spectroscopically confirmed RSGs and other NGC 6822 content studies and discuss our overall completeness, concluding that radial velocity alone is an insufficient method of determining membership in NGC 6822. After transforming the J and K magnitudes to effective temperatures and luminosities, we compare these physical properties with predictions from both the Geneva single-star and Binary Population and Spectral Synthesis (BPASS) single and binary star evolution tracks. We find that our derived temperatures and luminosities match the evolutionary model predictions well, however the BPASS model that includes the effects of binary evolution provides the best overall fit. This revealed the presence of a group of cool RSGs in NGC 6822, suggesting a history of binary interaction. We hope this work will lead to further comparative RSG studies in other Local Group galaxies, opportunities for direct spectroscopic follow-up, and a better understanding of evolutionary model predictions.