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K2 was a community-driven NASA mission where all targets were proposed through guest observer programmes. Here we provide an overview of one of the largest of these endeavours, the K2 Galactic Archaeology Programme (K2GAP), with about 25 per cent of the observed targets being allocated to this programme. K2GAP provides asteroseismic parameters for about 23 000 giant stars across the Galaxy, which together with spectroscopic stellar parameters can give age and masses of stars. We discuss in detail the target selection procedure and provide a python program that implements the selection function (github.com/sanjibs/k2gap). Broadly speaking, the targets were selected on 2MASS colour J − Ks > 0.5, with finely tuned adjustments for each campaign. We discuss the detection completeness of the asteroseismic parameters νmax and Δν. About 14 per cent of giants were found to miss νmax detections and it was difficult to detect Δν for RC stars. Making use of the selection function, we compare the observed distribution of asteroseismic masses to theoretical predictions. The median asteroseismic mass is higher by about 4 per cent compared to predictions. We provide a selection-function-matched mock catalogue of stars based on a synthetic model of the Galaxy for the community to use in subsequent analyses of the K2GAP data set (physics.usyd.edu.au/k2gap/download/).

 

ABSTRACT Precise asteroseismic parameters can be used to quickly estimate radius and mass distributions for large samples of stars. A number of automated methods are available to calculate the frequency of maximum acoustic power (νmax) and the frequency separation between overtone modes (Δν) from the power spectra of red giants. However, filtering through the results requires manual vetting, elaborate averaging across multiple methods or sharp cuts in certain parameters to ensure robust samples of stars free of outliers. Given the importance of ensemble studies for Galactic archaeology and the surge in data availability, faster methods for obtaining reliable asteroseismic parameters are desirable. We present a neural network classifier that vets Δν by combining multiple features from the visual Δν vetting process. Our classifier is able to analyse large numbers of stars, determining whether their measured Δν are reliable and thus delivering clean samples of oscillating stars with minimal effort. Our classifier is independent of the method used to obtain νmax and Δν, and therefore can be applied as a final step to any such method. Tests of our classifier’s performance on manually vetted Δν measurements reach an accuracy of 95 per cent. We apply the method to giants observed by the K2 Galactic Archaeology Program and find that our results retain stars with astrophysical oscillation parameters consistent with the parameter distributions already defined by well-characterized Kepler red giants. 

The vast majority of Milky Way stellar halo stars were likely accreted from a small number (<~3) of relatively large dwarf galaxy accretion events. However, the timing of these events is poorly constrained and predominantly relies on indirect dynamical mixing arguments or imprecise age measurements of stars associated with debris structures. Here, we aim to infer robust stellar ages for stars associated with galactic substructures to more directly constrain the merger history of the Galaxy. By combining kinematic, asteroseismic, and spectroscopic data where available, we infer stellar ages for a sample of 10 red giant stars that were kinematically selected to be within the stellar halo, a subset of which are associated with the Gaia–Enceladus–Sausage halo substructure, and compare their ages to 3 red giant stars in the Galactic disk. Despite systematic differences in both absolute and relative ages determined here, age rankings of stars in this sample are robust. Passing the same observable inputs to multiple stellar age determination packages, we measure a weighted average age for the Gaia–Enceladus–Sausage stars in our sample of 8+/-3 (stat.)+/-1 (sys.) Gyr. We also determine hierarchical ages using isochrones for the populations of Gaia–Enceladus–Sausage, in situ halo and disk stars, finding a Gaia–Enceladus–Sausage population age of 8.0+2.3-3.2 Gyr. Although we cannot distinguish hierarchical population ages of halo or disk structures with our limited data and sample of stars, this framework should allow a distinct characterization of Galactic substructures using larger stellar samples and additional data available in the near future 

We present the third and final data release of the K2 Galactic Archaeology Program (K2 GAP) for Campaigns C1–C8 and C10–C18. We provide asteroseismic radius and mass coefficients,κ_Randκ_M, for ∼19,000 red giant stars, which translate directly to radius and mass given a temperature. As such, K2 GAP DR3 represents the largest asteroseismic sample in the literature to date. K2 GAP DR3 stellar parameters are calibrated to be on an absolute parallactic scale based on Gaia DR2, with red giant branch and red clump evolutionary state classifications provided via a machine-learning approach. Combining these stellar parameters with GALAH DR3 spectroscopy, we determine asteroseismic ages with precisions of ∼20%–30% and compare age-abundance relations to Galactic chemical evolution models among both low- and high-αpopulations forα, light, iron-peak, and neutron-capture elements. We confirm recent indications in the literature of both increased Ba production at late Galactic times as well as significant contributions tor-process enrichment from prompt sources associated with, e.g., core-collapse supernovae. With an eye toward other Galactic archeology applications, we characterize K2 GAP DR3 uncertainties and completeness using injection tests, suggesting that K2 GAP DR3 is largely unbiased in mass/age, with uncertainties of 2.9% (stat.) ± 0.1% (syst.) and 6.7% (stat.) ± 0.3% (syst.) inκ_Randκ_Mfor red giant branch stars and 4.7% (stat.) ± 0.3% (syst.) and 11% (stat.) ± 0.9% (syst.) for red clump stars. We also identify percent-level asteroseismic systematics, which are likely related to the time baseline of the underlying data, and which therefore should be considered in TESS asteroseismic analysis.