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  Clumpiness: time-domain classification of red giant evolutionary states

Kuszlewicz, J. S., Hekker, S., & Bell, K. J. (2020). Clumpiness: time-domain classification of red giant evolutionary states. Monthly Notices of the Royal Astronomical Society, 497(4), 4843-4856. doi:10.1093/mnras/staa2155.

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 Creators:
Kuszlewicz, James S.1, Author              
Hekker, Saskia1, Author              
Bell, Keaton J.1, Author              
Affiliations:
1Max Planck Research Group in Stellar Ages and Galactic Evolution (SAGE), Max Planck Institute for Solar System Research, Max Planck Society, ou_2265636              

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Free keywords: asteroseismology, methods: data analysis, methods: statistical
 Abstract: Long, high-quality time-series data provided by previous space missions such as CoRoT and Kepler have made it possible to derive the evolutionary state of red giant stars, i.e. whether the stars are hydrogen-shell burning around an inert helium core or helium-core burning, from their individual oscillation modes. We utilize data from the Kepler mission to develop a tool to classify the evolutionary state for the large number of stars being observed in the current era of K2, TESS, and for the future PLATO mission. These missions provide new challenges for evolutionary state classification given the large number of stars being observed and the shorter observing duration of the data. We propose a new method, Clumpiness, based upon a supervised classification scheme that uses `summary statistics' of the time series, combined with distance information from the Gaia mission to predict the evolutionary state. Applying this to red giants in the APOKASC catalogue, we obtain a classification accuracy of similar to 91 per cent for the full 4 yr of Kepler data, for those stars that are either only hydrogen-shell burning or also helium-core burning. We also applied the method to shorter Kepler data sets, mimicking CoRoT, K2, and TESS achieving an accuracy > 91 per cent even for the 27 d time series. This work paves the way towards fast, reliable classification of vast amounts of relatively short-time-span data with a few, well-engineered features.

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Language(s): eng - English
 Dates: 2020
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1093/mnras/staa2155
 Degree: -

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Title: Monthly Notices of the Royal Astronomical Society
  Other : Mon. Not. R. Astron. Soc.
Source Genre: Journal
 Creator(s):
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 497 (4) Sequence Number: - Start / End Page: 4843 - 4856 Identifier: ISSN: 1365-8711
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000024150