The Bayesian Asteroseismology data Modeling pipeline and its application to $\it K2$ data

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 ${\it K2}$ 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 ${\it Kepler}$ and ${\it TESS}$. We demonstrate the BAM oscillation parameters are consistent within $\sim 1.53\%\ (\mathrm{random}) \pm 0.2\%\ (\mathrm{systematic})$ and $1.51\%\ (\mathrm{random}) \pm 0.6\%\ (\mathrm{systematic})$ for $\nu_{\mathrm{max}}$ and $\Delta \nu$ with benchmark results for typical ${\it K2}$ red giant stars in the ${\it K2}$ Galactic Archaeology Program's (GAP) Campaign 1 sample. Application of BAM to $13016$ ${\it K2}$ Campaign 1 targets not in the GAP sample yields $104$ red giant solar-like oscillators. Based on the number of serendipitous giants we find, we estimate an upper limit on the average purity in dwarf selection among C1 proposals is $\approx 99\%$, which could be lower when considering incompleteness in BAM detection efficiency, and proper motion cuts specific to C1 Guest Observer proposals.