---
_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: '11618'
abstract:
- lang: eng
  text: Asteroseismology provides global stellar parameters such as masses, radii,
    or surface gravities using mean global seismic parameters and effective temperature
    for thousands of low-mass stars (0.8 M⊙ < M < 3 M⊙). This methodology has been
    successfully applied to stars in which acoustic modes excited by turbulent convection
    are measured. Other methods such as the Flicker technique can also be used to
    determine stellar surface gravities, but only works for log g above 2.5 dex. In
    this work, we present a new metric called FliPer (Flicker in spectral power density,
    in opposition to the standard Flicker measurement which is computed in the time
    domain); it is able to extend the range for which reliable surface gravities can
    be obtained (0.1 < log g < 4.6 dex) without performing any seismic analysis for
    stars brighter than Kp < 14. FliPer takes into account the average variability
    of a star measured in the power density spectrum in a given range of frequencies.
    However, FliPer values calculated on several ranges of frequency are required
    to better characterize a star. Using a large set of asteroseismic targets it is
    possible to calibrate the behavior of surface gravity with FliPer through machine
    learning. This calibration made with a random forest regressor covers a wide range
    of surface gravities from main-sequence stars to subgiants and red giants, with
    very small uncertainties from 0.04 to 0.1 dex. FliPer values can be inserted in
    automatic global seismic pipelines to either give an estimation of the stellar
    surface gravity or to assess the quality of the seismic results by detecting any
    outliers in the obtained νmax values. FliPer also constrains the surface gravities
    of main-sequence dwarfs using only long-cadence data for which the Nyquist frequency
    is too low to measure the acoustic-mode properties.
acknowledgement: We thank the anonymous referee for the very useful comments. We would
  also like to thank M. Benbakoura for his help in analyzing the light curves of several
  binary systems included in our set of stars. L.B. and R.A.G. acknowledge the support
  from PLATO and GOLF CNES grants. S.M. acknowledges support from the National Aeronautics
  and Space Administration under Grant NNX15AF13G, the National Science Foundation
  grant AST-1411685, and the Ramon y Cajal fellowship no. RYC-2015-17697. E.C. is
  funded by the European Union’s Horizon 2020 research and innovation program under
  the Marie Sklodowska-Curie grant agreement no. 664931. O.J.H and B.M.R. acknowledge
  the support of the UK Science and Technology Facilities Council (STFC). Funding
  for the Stellar Astrophysics Centre is provided by the Danish National Research
  Foundation (Grant DNRF106). This research has made use of NASA’s Astrophysics Data
  System. Data presented in this paper were obtained from the Mikulski Archive for
  Space Telescopes (MAST). STScI is operated by the Association of Universities for
  Research in Astronomy, Inc., under NASA contract NAS5-26555.
article_number: A38
article_processing_charge: No
article_type: original
arxiv: 1
author:
- 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: R. A.
  full_name: García, R. A.
  last_name: García
- first_name: G. R.
  full_name: Davies, G. R.
  last_name: Davies
- first_name: S.
  full_name: Mathur, S.
  last_name: Mathur
- first_name: E.
  full_name: Corsaro, E.
  last_name: Corsaro
- first_name: O. J.
  full_name: Hall, O. J.
  last_name: Hall
- first_name: B. M.
  full_name: Rendle, B. M.
  last_name: Rendle
citation:
  ama: 'Bugnet LA, García RA, Davies GR, et al. FliPer: A global measure of power
    density to estimate surface gravities of main-sequence solar-like stars and red
    giants. <i>Astronomy &#38; Astrophysics</i>. 2018;620. doi:<a href="https://doi.org/10.1051/0004-6361/201833106">10.1051/0004-6361/201833106</a>'
  apa: 'Bugnet, L. A., García, R. A., Davies, G. R., Mathur, S., Corsaro, E., Hall,
    O. J., &#38; Rendle, B. M. (2018). FliPer: A global measure of power density to
    estimate surface gravities of main-sequence solar-like stars and red giants. <i>Astronomy
    &#38; Astrophysics</i>. EDP Sciences. <a href="https://doi.org/10.1051/0004-6361/201833106">https://doi.org/10.1051/0004-6361/201833106</a>'
  chicago: 'Bugnet, Lisa Annabelle, R. A. García, G. R. Davies, S. Mathur, E. Corsaro,
    O. J. Hall, and B. M. Rendle. “FliPer: A Global Measure of Power Density to Estimate
    Surface Gravities of Main-Sequence Solar-like Stars and Red Giants.” <i>Astronomy
    &#38; Astrophysics</i>. EDP Sciences, 2018. <a href="https://doi.org/10.1051/0004-6361/201833106">https://doi.org/10.1051/0004-6361/201833106</a>.'
  ieee: 'L. A. Bugnet <i>et al.</i>, “FliPer: A global measure of power density to
    estimate surface gravities of main-sequence solar-like stars and red giants,”
    <i>Astronomy &#38; Astrophysics</i>, vol. 620. EDP Sciences, 2018.'
  ista: 'Bugnet LA, García RA, Davies GR, Mathur S, Corsaro E, Hall OJ, Rendle BM.
    2018. FliPer: A global measure of power density to estimate surface gravities
    of main-sequence solar-like stars and red giants. Astronomy &#38; Astrophysics.
    620, A38.'
  mla: 'Bugnet, Lisa Annabelle, et al. “FliPer: A Global Measure of Power Density
    to Estimate Surface Gravities of Main-Sequence Solar-like Stars and Red Giants.”
    <i>Astronomy &#38; Astrophysics</i>, vol. 620, A38, EDP Sciences, 2018, doi:<a
    href="https://doi.org/10.1051/0004-6361/201833106">10.1051/0004-6361/201833106</a>.'
  short: L.A. Bugnet, R.A. García, G.R. Davies, S. Mathur, E. Corsaro, O.J. Hall,
    B.M. Rendle, Astronomy &#38; Astrophysics 620 (2018).
date_created: 2022-07-18T14:37:39Z
date_published: 2018-12-01T00:00:00Z
date_updated: 2022-08-22T07:41:07Z
day: '01'
doi: 10.1051/0004-6361/201833106
extern: '1'
external_id:
  arxiv:
  - '1809.05105'
intvolume: '       620'
keyword:
- Space and Planetary Science
- Astronomy and Astrophysics
- asteroseismology / methods
- data analysis / stars
- oscillations
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1809.05105
month: '12'
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: 'FliPer: A global measure of power density to estimate surface gravities of
  main-sequence solar-like stars and red giants'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 620
year: '2018'
...
---
_id: '11631'
abstract:
- lang: eng
  text: "The recently launched NASA Transiting Exoplanet Survey Satellite (TESS) mission
    is going to collect lightcurves for a few hundred million of stars and we expect
    to increase the number of pulsating stars to analyze compared to the few thousand
    stars observed by the CoRoT, Kepler and K2 missions. However, most of the TESS
    targets have not yet been properly classified and characterized. In order to improve
    the analysis of the TESS data, it is crucial to determine the type of stellar
    pulsations in a timely manner. We propose an automatic method to classify stars
    attending to their pulsation properties, in particular, to identify solar-like
    pulsators among all TESS targets. It relies on the use of the global amount of
    power contained in the power spectrum (already known as the FliPer method) as
    a key parameter, along with\r\nthe effective temperature, to feed into a machine
    learning classifier. Our study, based on TESS simulated datasets, shows that we
    are able to classify pulsators with a 98% accuracy."
article_number: '1811.12140'
article_processing_charge: No
arxiv: 1
author:
- 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: R. A.
  full_name: García, R. A.
  last_name: García
- first_name: G. R.
  full_name: Davies, G. R.
  last_name: Davies
- first_name: S.
  full_name: Mathur, S.
  last_name: Mathur
- first_name: O. J.
  full_name: Hall, O. J.
  last_name: Hall
- first_name: B. M.
  full_name: Rendle, B. M.
  last_name: Rendle
citation:
  ama: 'Bugnet LA, García RA, Davies GR, Mathur S, Hall OJ, Rendle BM. FliPer: Classifying
    TESS pulsating stars. <i>arXiv</i>. doi:<a href="https://doi.org/10.48550/arXiv.1811.12140">10.48550/arXiv.1811.12140</a>'
  apa: 'Bugnet, L. A., García, R. A., Davies, G. R., Mathur, S., Hall, O. J., &#38;
    Rendle, B. M. (n.d.). FliPer: Classifying TESS pulsating stars. <i>arXiv</i>.
    <a href="https://doi.org/10.48550/arXiv.1811.12140">https://doi.org/10.48550/arXiv.1811.12140</a>'
  chicago: 'Bugnet, Lisa Annabelle, R. A. García, G. R. Davies, S. Mathur, O. J. Hall,
    and B. M. Rendle. “FliPer: Classifying TESS Pulsating Stars.” <i>ArXiv</i>, n.d.
    <a href="https://doi.org/10.48550/arXiv.1811.12140">https://doi.org/10.48550/arXiv.1811.12140</a>.'
  ieee: 'L. A. Bugnet, R. A. García, G. R. Davies, S. Mathur, O. J. Hall, and B. M.
    Rendle, “FliPer: Classifying TESS pulsating stars,” <i>arXiv</i>. .'
  ista: 'Bugnet LA, García RA, Davies GR, Mathur S, Hall OJ, Rendle BM. FliPer: Classifying
    TESS pulsating stars. arXiv, 1811.12140.'
  mla: 'Bugnet, Lisa Annabelle, et al. “FliPer: Classifying TESS Pulsating Stars.”
    <i>ArXiv</i>, 1811.12140, doi:<a href="https://doi.org/10.48550/arXiv.1811.12140">10.48550/arXiv.1811.12140</a>.'
  short: L.A. Bugnet, R.A. García, G.R. Davies, S. Mathur, O.J. Hall, B.M. Rendle,
    ArXiv (n.d.).
date_created: 2022-07-21T07:05:23Z
date_published: 2018-11-29T00:00:00Z
date_updated: 2022-08-22T08:41:55Z
day: '29'
doi: 10.48550/arXiv.1811.12140
extern: '1'
external_id:
  arxiv:
  - '1811.12140'
keyword:
- asteroseismology - methods
- data analysis - stars
- oscillations
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: ' https://doi.org/10.48550/arXiv.1811.12140'
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: 'FliPer: Classifying TESS pulsating stars'
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2018'
...
---
_id: '11633'
abstract:
- lang: eng
  text: Our understanding of stars through asteroseismic data analysis is limited
    by our ability to take advantage of the huge amount of observed stars provided
    by space missions such as CoRoT, Kepler , K2, and soon TESS and PLATO. Global
    seismic pipelines provide global stellar parameters such as mass and radius using
    the mean seismic parameters, as well as the effective temperature. These pipelines
    are commonly used automatically on thousands of stars observed by K2 for 3 months
    (and soon TESS for at least ∼ 1 month). However, pipelines are not immune from
    misidentifying noise peaks and stellar oscillations. Therefore, new validation
    techniques are required to assess the quality of these results. We present a new
    metric called FliPer (Flicker in Power), which takes into account the average
    variability at all measured time scales. The proper calibration of FliPer enables
    us to obtain good estimations of global stellar parameters such as surface gravity
    that are robust against the influence of noise peaks and hence are an excellent
    way to find faults in asteroseismic pipelines.
article_number: '1711.02890'
article_processing_charge: No
arxiv: 1
author:
- 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: R. A.
  full_name: Garcia, R. A.
  last_name: Garcia
- first_name: G. R.
  full_name: Davies, G. R.
  last_name: Davies
- first_name: S.
  full_name: Mathur, S.
  last_name: Mathur
- first_name: E.
  full_name: Corsaro, E.
  last_name: Corsaro
citation:
  ama: 'Bugnet LA, Garcia RA, Davies GR, Mathur S, Corsaro E. FliPer: Checking the
    reliability of global seismic parameters from automatic pipelines. <i>arXiv</i>.
    doi:<a href="https://doi.org/10.48550/arXiv.1711.02890">10.48550/arXiv.1711.02890</a>'
  apa: 'Bugnet, L. A., Garcia, R. A., Davies, G. R., Mathur, S., &#38; Corsaro, E.
    (n.d.). FliPer: Checking the reliability of global seismic parameters from automatic
    pipelines. <i>arXiv</i>. <a href="https://doi.org/10.48550/arXiv.1711.02890">https://doi.org/10.48550/arXiv.1711.02890</a>'
  chicago: 'Bugnet, Lisa Annabelle, R. A. Garcia, G. R. Davies, S. Mathur, and E.
    Corsaro. “FliPer: Checking the Reliability of Global Seismic Parameters from Automatic
    Pipelines.” <i>ArXiv</i>, n.d. <a href="https://doi.org/10.48550/arXiv.1711.02890">https://doi.org/10.48550/arXiv.1711.02890</a>.'
  ieee: 'L. A. Bugnet, R. A. Garcia, G. R. Davies, S. Mathur, and E. Corsaro, “FliPer:
    Checking the reliability of global seismic parameters from automatic pipelines,”
    <i>arXiv</i>. .'
  ista: 'Bugnet LA, Garcia RA, Davies GR, Mathur S, Corsaro E. FliPer: Checking the
    reliability of global seismic parameters from automatic pipelines. arXiv, 1711.02890.'
  mla: 'Bugnet, Lisa Annabelle, et al. “FliPer: Checking the Reliability of Global
    Seismic Parameters from Automatic Pipelines.” <i>ArXiv</i>, 1711.02890, doi:<a
    href="https://doi.org/10.48550/arXiv.1711.02890">10.48550/arXiv.1711.02890</a>.'
  short: L.A. Bugnet, R.A. Garcia, G.R. Davies, S. Mathur, E. Corsaro, ArXiv (n.d.).
date_created: 2022-07-21T07:13:13Z
date_published: 2017-11-08T00:00:00Z
date_updated: 2022-08-22T08:45:42Z
day: '08'
doi: 10.48550/arXiv.1711.02890
extern: '1'
external_id:
  arxiv:
  - '1711.02890'
keyword:
- asteroseismology - methods
- data analysis - stars
- oscillations
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.1711.02890
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: 'FliPer: Checking the reliability of global seismic parameters from automatic
  pipelines'
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2017'
...