Improving transit characterisation with Gaussian process modelling of stellar variability

Autores
Barros, S. C. C.; Demangeon, O.; Diaz, Rodrigo Fernando; Cabrera, J.; Santos, N. C.; Faria, J. P.; Pereira, F.
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Context. New photometric space missions to detect and characterise transiting exoplanets are focusing on bright stars to obtain high cadence, high signal-to-noise light curves. Since these missions will be sensitive to stellar oscillations and granulation even for dwarf stars, they will be limited by stellar variability. Therefore, it is crucial and timely to develop robust methods to account for and correct for stellar variability. Aims. We tested the performance of Gaussian process (GP) regression on the characterisation of transiting planets, and in particular to determine how many components of variability are necessary to describe high cadence, high signal-to-noise light curves expected from CHEOPS and PLATO. To achieve this, we selected a sample of bright stars observed in the asteroseismology field of CoRoT at high cadence (32 s) and high signal-to-noise ratio (S/N). Methods. We used GPs to model stellar variability including different combinations of stellar oscillations, granulation, and rotational modulation models. We preformed model comparison to find the best activity model fit to our data. We compared the best multi-component model with the usual one-component model used for transit retrieval and with a non-GP model. Results. We found that the best GP stellar variability model contains four to five variability components: one stellar oscillation component, two to four granulation components, and/or one rotational modulation component, which is consistent with results from asteroseismology. However, this high number of components is in contrast with the one-component GP model (1GP) commonly used in the literature for transit characterisation. Therefore, we compared the performance of the best multi-component GP model with the 1GP model in the derivation of transit parameters of simulated transits. We found that for Jupiter-and Neptune-size planets the best multi-component GP model is slightly better than the 1GP model, and much better than the non-GP model that gives biased results. For Earth-size planets, the 1GP model fails to retrieve the transit because it is a poor description of stellar activity. The non-GP model gives some biased results and the best multi-component GP is capable of retrieving the correct transit model parameters. Conclusions. We conclude that when characterising transiting exoplanets with high S/Ns and high cadence light curves, we need models that couple the description of stellar variability with the transits analysis, like GPs. Moreover, for Earth-like exoplanets a better description of stellar variability (achieved using multi-component models) improves the planetary characterisation. Our results are particularly important for the analysis of TESS, CHEOPS, and PLATO light curves.
Fil: Barros, S. C. C.. Universidad de Porto; Portugal
Fil: Demangeon, O.. Universidad de Porto; Portugal
Fil: Diaz, Rodrigo Fernando. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
Fil: Cabrera, J.. No especifíca;
Fil: Santos, N. C.. Universidad de Porto; Portugal
Fil: Faria, J. P.. Universidad de Porto; Portugal
Fil: Pereira, F.. Universidad de Porto; Portugal
Materia
METHODS: DATA ANALYSIS
PLANETS AND SATELLITES: COMPOSITION
PLANETS AND SATELLITES: FUNDAMENTAL PARAMETERS
STARS: ACTIVITY
TECHNIQUES: PHOTOMETRIC
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/168157

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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Improving transit characterisation with Gaussian process modelling of stellar variabilityBarros, S. C. C.Demangeon, O.Diaz, Rodrigo FernandoCabrera, J.Santos, N. C.Faria, J. P.Pereira, F.METHODS: DATA ANALYSISPLANETS AND SATELLITES: COMPOSITIONPLANETS AND SATELLITES: FUNDAMENTAL PARAMETERSSTARS: ACTIVITYTECHNIQUES: PHOTOMETRIChttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Context. New photometric space missions to detect and characterise transiting exoplanets are focusing on bright stars to obtain high cadence, high signal-to-noise light curves. Since these missions will be sensitive to stellar oscillations and granulation even for dwarf stars, they will be limited by stellar variability. Therefore, it is crucial and timely to develop robust methods to account for and correct for stellar variability. Aims. We tested the performance of Gaussian process (GP) regression on the characterisation of transiting planets, and in particular to determine how many components of variability are necessary to describe high cadence, high signal-to-noise light curves expected from CHEOPS and PLATO. To achieve this, we selected a sample of bright stars observed in the asteroseismology field of CoRoT at high cadence (32 s) and high signal-to-noise ratio (S/N). Methods. We used GPs to model stellar variability including different combinations of stellar oscillations, granulation, and rotational modulation models. We preformed model comparison to find the best activity model fit to our data. We compared the best multi-component model with the usual one-component model used for transit retrieval and with a non-GP model. Results. We found that the best GP stellar variability model contains four to five variability components: one stellar oscillation component, two to four granulation components, and/or one rotational modulation component, which is consistent with results from asteroseismology. However, this high number of components is in contrast with the one-component GP model (1GP) commonly used in the literature for transit characterisation. Therefore, we compared the performance of the best multi-component GP model with the 1GP model in the derivation of transit parameters of simulated transits. We found that for Jupiter-and Neptune-size planets the best multi-component GP model is slightly better than the 1GP model, and much better than the non-GP model that gives biased results. For Earth-size planets, the 1GP model fails to retrieve the transit because it is a poor description of stellar activity. The non-GP model gives some biased results and the best multi-component GP is capable of retrieving the correct transit model parameters. Conclusions. We conclude that when characterising transiting exoplanets with high S/Ns and high cadence light curves, we need models that couple the description of stellar variability with the transits analysis, like GPs. Moreover, for Earth-like exoplanets a better description of stellar variability (achieved using multi-component models) improves the planetary characterisation. Our results are particularly important for the analysis of TESS, CHEOPS, and PLATO light curves.Fil: Barros, S. C. C.. Universidad de Porto; PortugalFil: Demangeon, O.. Universidad de Porto; PortugalFil: Diaz, Rodrigo Fernando. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Cabrera, J.. No especifíca;Fil: Santos, N. C.. Universidad de Porto; PortugalFil: Faria, J. P.. Universidad de Porto; PortugalFil: Pereira, F.. Universidad de Porto; PortugalEDP Sciences2020-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/168157Barros, S. C. C.; Demangeon, O.; Diaz, Rodrigo Fernando; Cabrera, J.; Santos, N. C.; et al.; Improving transit characterisation with Gaussian process modelling of stellar variability; EDP Sciences; Astronomy and Astrophysics; 634; 2-2020; 1-130004-6361CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1051/0004-6361/201936086info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:45:02Zoai:ri.conicet.gov.ar:11336/168157instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:45:03.17CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Improving transit characterisation with Gaussian process modelling of stellar variability
title Improving transit characterisation with Gaussian process modelling of stellar variability
spellingShingle Improving transit characterisation with Gaussian process modelling of stellar variability
Barros, S. C. C.
METHODS: DATA ANALYSIS
PLANETS AND SATELLITES: COMPOSITION
PLANETS AND SATELLITES: FUNDAMENTAL PARAMETERS
STARS: ACTIVITY
TECHNIQUES: PHOTOMETRIC
title_short Improving transit characterisation with Gaussian process modelling of stellar variability
title_full Improving transit characterisation with Gaussian process modelling of stellar variability
title_fullStr Improving transit characterisation with Gaussian process modelling of stellar variability
title_full_unstemmed Improving transit characterisation with Gaussian process modelling of stellar variability
title_sort Improving transit characterisation with Gaussian process modelling of stellar variability
dc.creator.none.fl_str_mv Barros, S. C. C.
Demangeon, O.
Diaz, Rodrigo Fernando
Cabrera, J.
Santos, N. C.
Faria, J. P.
Pereira, F.
author Barros, S. C. C.
author_facet Barros, S. C. C.
Demangeon, O.
Diaz, Rodrigo Fernando
Cabrera, J.
Santos, N. C.
Faria, J. P.
Pereira, F.
author_role author
author2 Demangeon, O.
Diaz, Rodrigo Fernando
Cabrera, J.
Santos, N. C.
Faria, J. P.
Pereira, F.
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv METHODS: DATA ANALYSIS
PLANETS AND SATELLITES: COMPOSITION
PLANETS AND SATELLITES: FUNDAMENTAL PARAMETERS
STARS: ACTIVITY
TECHNIQUES: PHOTOMETRIC
topic METHODS: DATA ANALYSIS
PLANETS AND SATELLITES: COMPOSITION
PLANETS AND SATELLITES: FUNDAMENTAL PARAMETERS
STARS: ACTIVITY
TECHNIQUES: PHOTOMETRIC
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Context. New photometric space missions to detect and characterise transiting exoplanets are focusing on bright stars to obtain high cadence, high signal-to-noise light curves. Since these missions will be sensitive to stellar oscillations and granulation even for dwarf stars, they will be limited by stellar variability. Therefore, it is crucial and timely to develop robust methods to account for and correct for stellar variability. Aims. We tested the performance of Gaussian process (GP) regression on the characterisation of transiting planets, and in particular to determine how many components of variability are necessary to describe high cadence, high signal-to-noise light curves expected from CHEOPS and PLATO. To achieve this, we selected a sample of bright stars observed in the asteroseismology field of CoRoT at high cadence (32 s) and high signal-to-noise ratio (S/N). Methods. We used GPs to model stellar variability including different combinations of stellar oscillations, granulation, and rotational modulation models. We preformed model comparison to find the best activity model fit to our data. We compared the best multi-component model with the usual one-component model used for transit retrieval and with a non-GP model. Results. We found that the best GP stellar variability model contains four to five variability components: one stellar oscillation component, two to four granulation components, and/or one rotational modulation component, which is consistent with results from asteroseismology. However, this high number of components is in contrast with the one-component GP model (1GP) commonly used in the literature for transit characterisation. Therefore, we compared the performance of the best multi-component GP model with the 1GP model in the derivation of transit parameters of simulated transits. We found that for Jupiter-and Neptune-size planets the best multi-component GP model is slightly better than the 1GP model, and much better than the non-GP model that gives biased results. For Earth-size planets, the 1GP model fails to retrieve the transit because it is a poor description of stellar activity. The non-GP model gives some biased results and the best multi-component GP is capable of retrieving the correct transit model parameters. Conclusions. We conclude that when characterising transiting exoplanets with high S/Ns and high cadence light curves, we need models that couple the description of stellar variability with the transits analysis, like GPs. Moreover, for Earth-like exoplanets a better description of stellar variability (achieved using multi-component models) improves the planetary characterisation. Our results are particularly important for the analysis of TESS, CHEOPS, and PLATO light curves.
Fil: Barros, S. C. C.. Universidad de Porto; Portugal
Fil: Demangeon, O.. Universidad de Porto; Portugal
Fil: Diaz, Rodrigo Fernando. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
Fil: Cabrera, J.. No especifíca;
Fil: Santos, N. C.. Universidad de Porto; Portugal
Fil: Faria, J. P.. Universidad de Porto; Portugal
Fil: Pereira, F.. Universidad de Porto; Portugal
description Context. New photometric space missions to detect and characterise transiting exoplanets are focusing on bright stars to obtain high cadence, high signal-to-noise light curves. Since these missions will be sensitive to stellar oscillations and granulation even for dwarf stars, they will be limited by stellar variability. Therefore, it is crucial and timely to develop robust methods to account for and correct for stellar variability. Aims. We tested the performance of Gaussian process (GP) regression on the characterisation of transiting planets, and in particular to determine how many components of variability are necessary to describe high cadence, high signal-to-noise light curves expected from CHEOPS and PLATO. To achieve this, we selected a sample of bright stars observed in the asteroseismology field of CoRoT at high cadence (32 s) and high signal-to-noise ratio (S/N). Methods. We used GPs to model stellar variability including different combinations of stellar oscillations, granulation, and rotational modulation models. We preformed model comparison to find the best activity model fit to our data. We compared the best multi-component model with the usual one-component model used for transit retrieval and with a non-GP model. Results. We found that the best GP stellar variability model contains four to five variability components: one stellar oscillation component, two to four granulation components, and/or one rotational modulation component, which is consistent with results from asteroseismology. However, this high number of components is in contrast with the one-component GP model (1GP) commonly used in the literature for transit characterisation. Therefore, we compared the performance of the best multi-component GP model with the 1GP model in the derivation of transit parameters of simulated transits. We found that for Jupiter-and Neptune-size planets the best multi-component GP model is slightly better than the 1GP model, and much better than the non-GP model that gives biased results. For Earth-size planets, the 1GP model fails to retrieve the transit because it is a poor description of stellar activity. The non-GP model gives some biased results and the best multi-component GP is capable of retrieving the correct transit model parameters. Conclusions. We conclude that when characterising transiting exoplanets with high S/Ns and high cadence light curves, we need models that couple the description of stellar variability with the transits analysis, like GPs. Moreover, for Earth-like exoplanets a better description of stellar variability (achieved using multi-component models) improves the planetary characterisation. Our results are particularly important for the analysis of TESS, CHEOPS, and PLATO light curves.
publishDate 2020
dc.date.none.fl_str_mv 2020-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/168157
Barros, S. C. C.; Demangeon, O.; Diaz, Rodrigo Fernando; Cabrera, J.; Santos, N. C.; et al.; Improving transit characterisation with Gaussian process modelling of stellar variability; EDP Sciences; Astronomy and Astrophysics; 634; 2-2020; 1-13
0004-6361
CONICET Digital
CONICET
url http://hdl.handle.net/11336/168157
identifier_str_mv Barros, S. C. C.; Demangeon, O.; Diaz, Rodrigo Fernando; Cabrera, J.; Santos, N. C.; et al.; Improving transit characterisation with Gaussian process modelling of stellar variability; EDP Sciences; Astronomy and Astrophysics; 634; 2-2020; 1-13
0004-6361
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1051/0004-6361/201936086
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv EDP Sciences
publisher.none.fl_str_mv EDP Sciences
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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