The Bayesian Asteroseismology data Modeling pipeline and its application to $\it K2$ data
Joel C. Zinn, Dennis Stello, Daniel Huber, Sanjib Sharma

TL;DR
The BAM pipeline automates the analysis of photometric time-series data to extract asteroseismic parameters, especially tailored for K2 data, and successfully identifies solar-like oscillators with high accuracy.
Contribution
We introduce BAM, a novel automated pipeline for asteroseismology that effectively processes K2 light curves and determines stellar oscillation parameters with high precision.
Findings
BAM achieves ~1.5% accuracy in oscillation parameters.
Identifies 104 new red giant solar-like oscillators in K2 data.
Estimates high purity in dwarf star selection among proposals.
Abstract
We present the Bayesian Asteroseismology data Modeling (BAM) pipeline, an automated asteroseismology pipeline that returns global oscillation parameters and granulation parameters from the analysis of photometric time-series. BAM also determines if a star is likely to be a solar-like oscillator. We have designed BAM to specially process light curves, which suffer from unique noise signatures that can confuse asteroseismic analysis, though it may be used on any photometric time series --- including those from and . We demonstrate the BAM oscillation parameters are consistent within and for and with benchmark results for typical red giant stars in the Galactic Archaeology…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
