Oscillation frequencies for 35 Kepler solar-type planet-hosting stars using Bayesian techniques and machine learning

G. R. Davies, V. Silva Aguirre, T. R. Bedding, R. Handberg, M. N. Lund, W. J. Chaplin, D. Huber, T. R. White, Othman Benomar, S. Hekker, S. Basu, T. L. Campante, J. Christensen-Dalsgaard, Y. Elsworth, C. Karoff, H. Kjeldsen, M. S. Lundkvist, T. S. Metcalfe, D. Stello

Research output: Contribution to journalArticle

Abstract

Kepler has revolutionized our understanding of both exoplanets and their host stars.Asteroseismology is a valuable tool in the characterization of stars and Kepler is an excellent observing facility to perform asteroseismology. Here we select a sample of 35 Kepler solar-type stars which host transiting exoplanets (or planet candidates) with detected solar-like oscillations. Using available Kepler short cadence data up to Quarter 16 we create power spectra optimized for asteroseismology of solar-type stars. We identify modes of oscillation and estimate mode frequencies by 'peak bagging' using a Bayesian Markov Chain Monte Carlo framework. In addition, we expand the methodology of quality assurance using a Bayesian unsupervised machine learning approach. We report the measured frequencies of the modes of oscillation for all 35 stars and frequency ratios commonly used in detailed asteroseismic modelling. Due to the high correlations associated with frequency ratios we report the covariance matrix of all frequencies measured and frequency ratios calculated. These frequencies, frequency ratios, and covariance matrices can be used to obtain tight constraint on the fundamental parameters of these planet-hosting stars.

Original languageEnglish (US)
Pages (from-to)2183-2195
Number of pages13
JournalMonthly Notices of the Royal Astronomical Society
Volume456
Issue number2
DOIs
StatePublished - Feb 21 2016

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machine learning
learning
planets
planet
oscillation
stars
oscillations
asteroseismology
matrix
Markov chain
extrasolar planets
methodology
solar oscillations
modeling
Markov chains
assurance
power spectra
estimates

Keywords

  • Asteroseismology
  • Planetary systems
  • Planets and satellites: fundamental parameters
  • Stars: evolution
  • Stars: fundamental parameters
  • Stars: oscillations

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

Cite this

Davies, G. R., Silva Aguirre, V., Bedding, T. R., Handberg, R., Lund, M. N., Chaplin, W. J., ... Stello, D. (2016). Oscillation frequencies for 35 Kepler solar-type planet-hosting stars using Bayesian techniques and machine learning. Monthly Notices of the Royal Astronomical Society, 456(2), 2183-2195. https://doi.org/10.1093/mnras/stv2593

Oscillation frequencies for 35 Kepler solar-type planet-hosting stars using Bayesian techniques and machine learning. / Davies, G. R.; Silva Aguirre, V.; Bedding, T. R.; Handberg, R.; Lund, M. N.; Chaplin, W. J.; Huber, D.; White, T. R.; Benomar, Othman; Hekker, S.; Basu, S.; Campante, T. L.; Christensen-Dalsgaard, J.; Elsworth, Y.; Karoff, C.; Kjeldsen, H.; Lundkvist, M. S.; Metcalfe, T. S.; Stello, D.

In: Monthly Notices of the Royal Astronomical Society, Vol. 456, No. 2, 21.02.2016, p. 2183-2195.

Research output: Contribution to journalArticle

Davies, GR, Silva Aguirre, V, Bedding, TR, Handberg, R, Lund, MN, Chaplin, WJ, Huber, D, White, TR, Benomar, O, Hekker, S, Basu, S, Campante, TL, Christensen-Dalsgaard, J, Elsworth, Y, Karoff, C, Kjeldsen, H, Lundkvist, MS, Metcalfe, TS & Stello, D 2016, 'Oscillation frequencies for 35 Kepler solar-type planet-hosting stars using Bayesian techniques and machine learning', Monthly Notices of the Royal Astronomical Society, vol. 456, no. 2, pp. 2183-2195. https://doi.org/10.1093/mnras/stv2593
Davies, G. R. ; Silva Aguirre, V. ; Bedding, T. R. ; Handberg, R. ; Lund, M. N. ; Chaplin, W. J. ; Huber, D. ; White, T. R. ; Benomar, Othman ; Hekker, S. ; Basu, S. ; Campante, T. L. ; Christensen-Dalsgaard, J. ; Elsworth, Y. ; Karoff, C. ; Kjeldsen, H. ; Lundkvist, M. S. ; Metcalfe, T. S. ; Stello, D. / Oscillation frequencies for 35 Kepler solar-type planet-hosting stars using Bayesian techniques and machine learning. In: Monthly Notices of the Royal Astronomical Society. 2016 ; Vol. 456, No. 2. pp. 2183-2195.
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