Measuring frequency and period separations in red-giant stars using machine learning

TL;DR

This paper presents a machine learning method to rapidly analyze red-giant star spectra, accurately extracting key asteroseismic parameters and discovering new candidates from large datasets like Kepler.

Contribution

The study introduces a novel machine learning algorithm that efficiently identifies red giants and measures seismic parameters from their spectra, enabling fast analysis of large stellar datasets.

Findings

Discovered ~25 new probable red giants in Kepler data.

Achieved parameter extraction from 1,000 spectra in about 5 seconds.

Validated results by comparing with traditional techniques across various stellar stages.

Abstract

Asteroseismology is used to infer the interior physics of stars. The \textit{Kepler} and TESS space missions have provided a vast data set of red-giant light curves, which may be used for asteroseismic analysis. These data sets are expected to significantly grow with future missions such as \textit{PLATO}, and efficient methods are therefore required to analyze these data rapidly. Here, we describe a machine learning algorithm that identifies red giants from the raw oscillation spectra and captures \textit{p} and \textit{mixed} mode parameters from the red-giant power spectra. We report algorithmic inferences for large frequency separation ($\Delta \nu$), frequency at maximum amplitude ($\nu_{max}$), and period separation ($\Delta \Pi$) for an ensemble of stars. In addition, we have discovered $\sim$25 new probable red giants among 151,000 \textit{Kepler} long-cadence stellar-oscillation spectra analyzed by the method, among which four are binary candidates which appear to possess red-giant counterparts. To validate the results of this method, we selected $\sim$ 3,000 \textit{Kepler} stars, at various evolutionary stages ranging from subgiants to red clumps, and compare inferences of $\Delta \nu$, $\Delta \Pi$, and $\nu_{max}$ with estimates obtained using other techniques. The power of the machine-learning algorithm lies in its speed: it is able to accurately extract seismic parameters from 1,000 spectra in $\sim$5 seconds on a modern computer (single core of the Intel Xeon Platinum 8280 CPU).