Classifying Kepler light curves for 12,000 A and F stars using supervised feature-based machine learning
| Autor: | Nicholas H Barbara, Timothy R Bedding, Ben D Fulcher, Simon J Murphy, Timothy Van Reeth |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
oscillations [stars]
RR LYRAE VARIABLES GAMMA DORADUS STARS PULSATING STARS FOS: Physical sciences FINDING BINARIES asteroseismology Astronomy & Astrophysics ECLIPSING BINARIES eclipsing [binaries] data analysis [methods] Astrophysics::Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics (astro-ph.IM) Astrophysics::Galaxy Astrophysics Solar and Stellar Astrophysics (astro-ph.SR) Science & Technology LUMINOSITIES Astronomy and Astrophysics PHASE MODULATION CATALOG Astrophysics - Solar and Stellar Astrophysics variables: general [stars] Space and Planetary Science Physical Sciences AUTOMATED CLASSIFICATION MODES Astrophysics::Earth and Planetary Astrophysics Astrophysics - Instrumentation and Methods for Astrophysics |
| Zdroj: | Barbara, N H, Bedding, T R, Fulcher, B D, Murphy, S J & Van Reeth, T 2022, ' Classifying Kepler light curves for 12 000 A and F stars using supervised feature-based machine learning ', Monthly Notices of the Royal Astronomical Society, vol. 514, no. 2, pp. 2793-2804 . https://doi.org/10.1093/mnras/stac1515 |
| DOI: | 10.48550/arxiv.2205.03020 |
| Popis: | With the availability of large-scale surveys like Kepler and TESS, there is a pressing need for automated methods to classify light curves according to known classes of variable stars. We introduce a new algorithm for classifying light curves that compares 7000 time-series features to find those which most effectively classify a given set of light curves. We apply our method to Kepler light curves for stars with effective temperatures in the range 6500--10,000K. We show that the sample can be meaningfully represented in an interpretable five-dimensional feature space that separates seven major classes of light curves (delta Scuti stars, gamma Doradus stars, RR Lyrae stars, rotational variables, contact eclipsing binaries, detached eclipsing binaries, and non-variables). We achieve a balanced classification accuracy of 82% on an independent test set of Kepler stars using a Gaussian mixture model classifier. We use our method to classify 12,000 Kepler light curves from Quarter 9 and provide a catalogue of the results. We further outline a confidence heuristic based on probability density with which to search our catalogue, and extract candidate lists of correctly-classified variable stars. Comment: published by MNRAS |
| Databáze: | OpenAIRE |
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