Insights from the APOKASC Determination of the Evolutionary State of Red-Giant Stars by consolidation of different methods

TL;DR

This study combines multiple asteroseismic and spectroscopic methods to accurately determine the evolutionary states of red giant stars, validating approaches and identifying unusual cases, thereby improving stellar characterization and modeling.

Contribution

It introduces a consensus-based approach to classify stellar evolutionary states by comparing diverse methods, enhancing validation and identifying peculiar stars.

Findings

High agreement between methods validates the approaches.

Identification of stars with strong p-g cavity coupling.

Refined uncertainty estimates for APOGEE temperature measurements.

Abstract

The internal working of low-mass stars is of great significance to both the study of stellar structure and the history of the Milky Way. Asteroseismology has the power to directly sense the internal structure of stars and allows for the determination of the evolutionary state -- i.e. has helium burning commenced or is the energy generated only by the fusion in the hydrogen-burning shell? We use observational data from red-giant stars in a combination (known as APOKASC) of asteroseismology (from the \textit{Kepler} mission) and spectroscopy (from SDSS/APOGEE). The new feature of the analysis is that the APOKASC evolutionary state determination is based on the comparison of diverse approaches to the investigation of the frequency-power spectrum. The high level of agreement between the methods is a strong validation of the approaches. Stars for which there is not a consensus view are readily identified. The comparison also facilitates the identification of unusual stars including those that show evidence for very strong coupling between p and g cavities. The comparison between the classification based on the spectroscopic data and asteroseismic data have led to a new value for the statistical uncertainty in APOGEE temperatures. These consensus evolutionary states will be used as an input for methods that derive masses and ages for these stars based on comparison of observables with stellar evolutionary models (`grid-based modeling') and as a training set for machine-learning and other data-driven methods of evolutionary state determination