---
_id: '11627'
abstract:
- lang: eng
  text: 'For a solar-like star, the surface rotation evolves with time, allowing in
    principle to estimate the age of a star from its surface rotation period. Here
    we are interested in measuring surface rotation periods of solar-like stars observed
    by the NASA mission Kepler. Different methods have been developed to track rotation
    signals in Kepler photometric light curves: time-frequency analysis based on wavelet
    techniques, autocorrelation and composite spectrum. We use the learning abilities
    of random forest classifiers to take decisions during two crucial steps of the
    analysis. First, given some input parameters, we discriminate the considered Kepler
    targets between rotating MS stars, non-rotating MS stars, red giants, binaries
    and pulsators. We then use a second classifier only on the MS rotating targets
    to decide the best data analysis treatment.'
article_number: '1906.09609'
article_processing_charge: No
arxiv: 1
author:
- first_name: S. N.
  full_name: Breton, S. N.
  last_name: Breton
- first_name: Lisa Annabelle
  full_name: Bugnet, Lisa Annabelle
  id: d9edb345-f866-11ec-9b37-d119b5234501
  last_name: Bugnet
  orcid: 0000-0003-0142-4000
- first_name: A. R. G.
  full_name: Santos, A. R. G.
  last_name: Santos
- first_name: A. Le
  full_name: Saux, A. Le
  last_name: Saux
- first_name: S.
  full_name: Mathur, S.
  last_name: Mathur
- first_name: P. L.
  full_name: Palle, P. L.
  last_name: Palle
- first_name: R. A.
  full_name: Garcia, R. A.
  last_name: Garcia
citation:
  ama: Breton SN, Bugnet LA, Santos ARG, et al. Determining surface rotation periods
    of solar-like stars observed by the Kepler mission using machine learning techniques.
    <i>arXiv</i>. doi:<a href="https://doi.org/10.48550/arXiv.1906.09609">10.48550/arXiv.1906.09609</a>
  apa: Breton, S. N., Bugnet, L. A., Santos, A. R. G., Saux, A. L., Mathur, S., Palle,
    P. L., &#38; Garcia, R. A. (n.d.). Determining surface rotation periods of solar-like
    stars observed by the Kepler mission using machine learning techniques. <i>arXiv</i>.
    <a href="https://doi.org/10.48550/arXiv.1906.09609">https://doi.org/10.48550/arXiv.1906.09609</a>
  chicago: Breton, S. N., Lisa Annabelle Bugnet, A. R. G. Santos, A. Le Saux, S. Mathur,
    P. L. Palle, and R. A. Garcia. “Determining Surface Rotation Periods of Solar-like
    Stars Observed by the Kepler Mission Using Machine Learning Techniques.” <i>ArXiv</i>,
    n.d. <a href="https://doi.org/10.48550/arXiv.1906.09609">https://doi.org/10.48550/arXiv.1906.09609</a>.
  ieee: S. N. Breton <i>et al.</i>, “Determining surface rotation periods of solar-like
    stars observed by the Kepler mission using machine learning techniques,” <i>arXiv</i>.
    .
  ista: Breton SN, Bugnet LA, Santos ARG, Saux AL, Mathur S, Palle PL, Garcia RA.
    Determining surface rotation periods of solar-like stars observed by the Kepler
    mission using machine learning techniques. arXiv, 1906.09609.
  mla: Breton, S. N., et al. “Determining Surface Rotation Periods of Solar-like Stars
    Observed by the Kepler Mission Using Machine Learning Techniques.” <i>ArXiv</i>,
    1906.09609, doi:<a href="https://doi.org/10.48550/arXiv.1906.09609">10.48550/arXiv.1906.09609</a>.
  short: S.N. Breton, L.A. Bugnet, A.R.G. Santos, A.L. Saux, S. Mathur, P.L. Palle,
    R.A. Garcia, ArXiv (n.d.).
date_created: 2022-07-20T11:18:53Z
date_published: 2019-06-23T00:00:00Z
date_updated: 2022-08-22T08:16:53Z
day: '23'
doi: 10.48550/arXiv.1906.09609
extern: '1'
external_id:
  arxiv:
  - '1906.09609'
keyword:
- asteroseismology
- rotation
- solar-like stars
- kepler
- machine learning
- random forest
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1906.09609
month: '06'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Determining surface rotation periods of solar-like stars observed by the Kepler
  mission using machine learning techniques
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2019'
...