---
_id: '11608'
abstract:
- lang: eng
  text: 'In order to understand stellar evolution, it is crucial to efficiently determine
    stellar surface rotation periods. Indeed, while they are of great importance in
    stellar models, angular momentum transport processes inside stars are still poorly
    understood today. Surface rotation, which is linked to the age of the star, is
    one of the constraints needed to improve the way those processes are modelled.
    Statistics of the surface rotation periods for a large sample of stars of different
    spectral types are thus necessary. An efficient tool to automatically determine
    reliable rotation periods is needed when dealing with large samples of stellar
    photometric datasets. The objective of this work is to develop such a tool. For
    this purpose, machine learning classifiers constitute relevant bases to build
    our new methodology. Random forest learning abilities are exploited to automate
    the extraction of rotation periods in Kepler light curves. Rotation periods and
    complementary parameters are obtained via three different methods: a wavelet analysis,
    the autocorrelation function of the light curve, and the composite spectrum. We
    trained three different classifiers: one to detect if rotational modulations are
    present in the light curve, one to flag close binary or classical pulsators candidates
    that can bias our rotation period determination, and finally one classifier to
    provide the final rotation period. We tested our machine learning pipeline on
    23 431 stars of the Kepler K and M dwarf reference rotation catalogue for which
    60% of the stars have been visually inspected. For the sample of 21 707 stars
    where all the input parameters are provided to the algorithm, 94.2% of them are
    correctly classified (as rotating or not). Among the stars that have a rotation
    period in the reference catalogue, the machine learning provides a period that
    agrees within 10% of the reference value for 95.3% of the stars. Moreover, the
    yield of correct rotation periods is raised to 99.5% after visually inspecting
    25.2% of the stars. Over the two main analysis steps, rotation classification
    and period selection, the pipeline yields a global agreement with the reference
    values of 92.1% and 96.9% before and after visual inspection. Random forest classifiers
    are efficient tools to determine reliable rotation periods in large samples of
    stars. The methodology presented here could be easily adapted to extract surface
    rotation periods for stars with different spectral types or observed by other
    instruments such as K2, TESS or by PLATO in the near future.'
acknowledgement: 'We thank Suzanne Aigrain and Joe Llama for providing us with the
  simulated data used in Aigrain et al. (2015). S. N. B., L. B. and R. A. G. acknowledge
  the support from PLATO and GOLF CNES grants. A. R. G. S. acknowledges the support
  from NASA under grant NNX17AF27G. S. M. acknowledges the support from the Spanish
  Ministry of Science and Innovation with the Ramon y Cajal fellowship number RYC-2015-17697.
  P. L. P. and S. M. acknowledge support from the Spanish Ministry of Science and
  Innovation with the grant number PID2019-107187GB-I00. This research has made use
  of the NASA Exoplanet Archive, which is operated by the California Institute of
  Technology, under contract with the National Aeronautics and Space Administration
  under the Exoplanet Exploration Program. Software: Python (Van Rossum & Drake 2009),
  numpy (Oliphant 2006), pandas (The pandas development team 2020; McKinney 2010),
  matplotlib (Hunter 2007), scikit-learn (Pedregosa et al. 2011). The source code
  used to obtain the present results can be found at: https://gitlab.com/sybreton/pushkin
  ; https://gitlab.com/sybreton/ml_surface_rotation_paper .'
article_number: A125
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: S. N.
  full_name: Breton, S. N.
  last_name: Breton
- first_name: A. R. G.
  full_name: Santos, A. R. G.
  last_name: Santos
- 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: S.
  full_name: Mathur, S.
  last_name: Mathur
- first_name: R. A.
  full_name: García, R. A.
  last_name: García
- first_name: P. L.
  full_name: Pallé, P. L.
  last_name: Pallé
citation:
  ama: 'Breton SN, Santos ARG, Bugnet LA, Mathur S, García RA, Pallé PL. ROOSTER:
    A machine-learning analysis tool for Kepler stellar rotation periods. <i>Astronomy
    &#38; Astrophysics</i>. 2021;647. doi:<a href="https://doi.org/10.1051/0004-6361/202039947">10.1051/0004-6361/202039947</a>'
  apa: 'Breton, S. N., Santos, A. R. G., Bugnet, L. A., Mathur, S., García, R. A.,
    &#38; Pallé, P. L. (2021). ROOSTER: A machine-learning analysis tool for Kepler
    stellar rotation periods. <i>Astronomy &#38; Astrophysics</i>. EDP Sciences. <a
    href="https://doi.org/10.1051/0004-6361/202039947">https://doi.org/10.1051/0004-6361/202039947</a>'
  chicago: 'Breton, S. N., A. R. G. Santos, Lisa Annabelle Bugnet, S. Mathur, R. A.
    García, and P. L. Pallé. “ROOSTER: A Machine-Learning Analysis Tool for Kepler
    Stellar Rotation Periods.” <i>Astronomy &#38; Astrophysics</i>. EDP Sciences,
    2021. <a href="https://doi.org/10.1051/0004-6361/202039947">https://doi.org/10.1051/0004-6361/202039947</a>.'
  ieee: 'S. N. Breton, A. R. G. Santos, L. A. Bugnet, S. Mathur, R. A. García, and
    P. L. Pallé, “ROOSTER: A machine-learning analysis tool for Kepler stellar rotation
    periods,” <i>Astronomy &#38; Astrophysics</i>, vol. 647. EDP Sciences, 2021.'
  ista: 'Breton SN, Santos ARG, Bugnet LA, Mathur S, García RA, Pallé PL. 2021. ROOSTER:
    A machine-learning analysis tool for Kepler stellar rotation periods. Astronomy
    &#38; Astrophysics. 647, A125.'
  mla: 'Breton, S. N., et al. “ROOSTER: A Machine-Learning Analysis Tool for Kepler
    Stellar Rotation Periods.” <i>Astronomy &#38; Astrophysics</i>, vol. 647, A125,
    EDP Sciences, 2021, doi:<a href="https://doi.org/10.1051/0004-6361/202039947">10.1051/0004-6361/202039947</a>.'
  short: S.N. Breton, A.R.G. Santos, L.A. Bugnet, S. Mathur, R.A. García, P.L. Pallé,
    Astronomy &#38; Astrophysics 647 (2021).
date_created: 2022-07-18T12:21:32Z
date_published: 2021-03-19T00:00:00Z
date_updated: 2022-08-22T08:47:47Z
day: '19'
doi: 10.1051/0004-6361/202039947
extern: '1'
external_id:
  arxiv:
  - '2101.10152'
intvolume: '       647'
keyword:
- Space and Planetary Science
- Astronomy and Astrophysics
- 'methods: data analysis / stars: solar-type / stars: activity / stars: rotation
  / starspots'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/2101.10152
month: '03'
oa: 1
oa_version: Preprint
publication: Astronomy & Astrophysics
publication_identifier:
  eissn:
  - 1432-0746
  issn:
  - 0004-6361
publication_status: published
publisher: EDP Sciences
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'ROOSTER: A machine-learning analysis tool for Kepler stellar rotation periods'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 647
year: '2021'
...
---
_id: '11623'
abstract:
- lang: eng
  text: Brightness variations due to dark spots on the stellar surface encode information
    about stellar surface rotation and magnetic activity. In this work, we analyze
    the Kepler long-cadence data of 26,521 main-sequence stars of spectral types M
    and K in order to measure their surface rotation and photometric activity level.
    Rotation-period estimates are obtained by the combination of a wavelet analysis
    and autocorrelation function of the light curves. Reliable rotation estimates
    are determined by comparing the results from the different rotation diagnostics
    and four data sets. We also measure the photometric activity proxy Sph using the
    amplitude of the flux variations on an appropriate timescale. We report rotation
    periods and photometric activity proxies for about 60% of the sample, including
    4431 targets for which McQuillan et al. did not report a rotation period. For
    the common targets with rotation estimates in this study and in McQuillan et al.,
    our rotation periods agree within 99%. In this work, we also identify potential
    polluters, such as misclassified red giants and classical pulsator candidates.
    Within the parameter range we study, there is a mild tendency for hotter stars
    to have shorter rotation periods. The photometric activity proxy spans a wider
    range of values with increasing effective temperature. The rotation period and
    photometric activity proxy are also related, with Sph being larger for fast rotators.
    Similar to McQuillan et al., we find a bimodal distribution of rotation periods.
acknowledgement: "The authors thank Róbert Szabó Paul G. Beck, Katrien Kolenberg,
  and Isabel L. Colman for helping on the classification of stars. This paper includes
  data collected by the Kepler mission and obtained from the MAST data archive at
  the Space Telescope Science Institute (STScI). Funding for the Kepler mission is
  provided by the National Aeronautics and Space Administration (NASA) Science Mission
  Directorate. STScI is operated by the Association of Universities for Research in
  Astronomy, Inc., under NASA contract NAS 5–26555. A.R.G.S. acknowledges the support
  from NASA under grant NNX17AF27G. R.A.G. and L.B. acknowledge the support from PLATO
  and GOLF CNES grants. S.M. acknowledges the support from the Ramon y Cajal fellowship
  number RYC-2015-17697. T.S.M. acknowledges support from a Visiting Fellowship at
  the Max Planck Institute for Solar System Research. This research has made use of
  the NASA Exoplanet Archive, which is operated by the California Institute of Technology,
  under contract with the National Aeronautics and Space Administration under the
  Exoplanet Exploration Program.\r\n\r\nSoftware: KADACS (García et al. 2011), NumPy
  (van der Walt et al. 2011), SciPy (Jones et al. 2001), Matplotlib (Hunter 2007).\r\n\r\nFacilities:
  MAST - , Kepler Eclipsing Binary Catalog - , Exoplanet Archive. -"
article_number: '21'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: A. R. G.
  full_name: Santos, A. R. G.
  last_name: Santos
- first_name: R. A.
  full_name: García, R. A.
  last_name: García
- first_name: S.
  full_name: Mathur, S.
  last_name: Mathur
- 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: J. L.
  full_name: van Saders, J. L.
  last_name: van Saders
- first_name: T. S.
  full_name: Metcalfe, T. S.
  last_name: Metcalfe
- first_name: G. V. A.
  full_name: Simonian, G. V. A.
  last_name: Simonian
- first_name: M. H.
  full_name: Pinsonneault, M. H.
  last_name: Pinsonneault
citation:
  ama: Santos ARG, García RA, Mathur S, et al. Surface rotation and photometric activity
    for Kepler targets. I. M and K main-sequence stars. <i>The Astrophysical Journal
    Supplement Series</i>. 2019;244(1). doi:<a href="https://doi.org/10.3847/1538-4365/ab3b56">10.3847/1538-4365/ab3b56</a>
  apa: Santos, A. R. G., García, R. A., Mathur, S., Bugnet, L. A., van Saders, J.
    L., Metcalfe, T. S., … Pinsonneault, M. H. (2019). Surface rotation and photometric
    activity for Kepler targets. I. M and K main-sequence stars. <i>The Astrophysical
    Journal Supplement Series</i>. IOP Publishing. <a href="https://doi.org/10.3847/1538-4365/ab3b56">https://doi.org/10.3847/1538-4365/ab3b56</a>
  chicago: Santos, A. R. G., R. A. García, S. Mathur, Lisa Annabelle Bugnet, J. L.
    van Saders, T. S. Metcalfe, G. V. A. Simonian, and M. H. Pinsonneault. “Surface
    Rotation and Photometric Activity for Kepler Targets. I. M and K Main-Sequence
    Stars.” <i>The Astrophysical Journal Supplement Series</i>. IOP Publishing, 2019.
    <a href="https://doi.org/10.3847/1538-4365/ab3b56">https://doi.org/10.3847/1538-4365/ab3b56</a>.
  ieee: A. R. G. Santos <i>et al.</i>, “Surface rotation and photometric activity
    for Kepler targets. I. M and K main-sequence stars,” <i>The Astrophysical Journal
    Supplement Series</i>, vol. 244, no. 1. IOP Publishing, 2019.
  ista: Santos ARG, García RA, Mathur S, Bugnet LA, van Saders JL, Metcalfe TS, Simonian
    GVA, Pinsonneault MH. 2019. Surface rotation and photometric activity for Kepler
    targets. I. M and K main-sequence stars. The Astrophysical Journal Supplement
    Series. 244(1), 21.
  mla: Santos, A. R. G., et al. “Surface Rotation and Photometric Activity for Kepler
    Targets. I. M and K Main-Sequence Stars.” <i>The Astrophysical Journal Supplement
    Series</i>, vol. 244, no. 1, 21, IOP Publishing, 2019, doi:<a href="https://doi.org/10.3847/1538-4365/ab3b56">10.3847/1538-4365/ab3b56</a>.
  short: A.R.G. Santos, R.A. García, S. Mathur, L.A. Bugnet, J.L. van Saders, T.S.
    Metcalfe, G.V.A. Simonian, M.H. Pinsonneault, The Astrophysical Journal Supplement
    Series 244 (2019).
date_created: 2022-07-19T09:21:58Z
date_published: 2019-09-19T00:00:00Z
date_updated: 2022-08-22T08:10:38Z
day: '19'
doi: 10.3847/1538-4365/ab3b56
extern: '1'
external_id:
  arxiv:
  - '1908.05222'
intvolume: '       244'
issue: '1'
keyword:
- Space and Planetary Science
- Astronomy and Astrophysics
- 'methods: data analysis'
- 'stars: activity'
- 'stars: low-mass'
- 'stars: rotation'
- starspots
- 'techniques: photometric'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1908.05222
month: '09'
oa: 1
oa_version: Preprint
publication: The Astrophysical Journal Supplement Series
publication_identifier:
  issn:
  - 0067-0049
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Surface rotation and photometric activity for Kepler targets. I. M and K main-sequence
  stars
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 244
year: '2019'
...