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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.
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The K2 Galactic Archaeology Program: Overview, target selection, and survey properties
ABSTRACT 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/).
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- Award ID(s):
- 2001869
- PAR ID:
- 10375879
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 517
- Issue:
- 2
- ISSN:
- 0035-8711
- Page Range / eLocation ID:
- p. 1970-1987
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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