A Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopy
Transmission spectroscopy, which consists of measuring the wavelength-dependent absorption of starlight by a planet's atmosphere during a transit, is a powerful probe of atmospheric composition. However, the expected signal is typically orders of magnitude smaller than instrumental systematics...
| Main Authors: | , , , , , |
|---|---|
| Format: | Journal article |
| Published: |
Oxford University Press
2012
|
| _version_ | 1797074381100285952 |
|---|---|
| author | Gibson, N Aigrain, S Roberts, S Evans, T Osborne, M Pont, F |
| author_facet | Gibson, N Aigrain, S Roberts, S Evans, T Osborne, M Pont, F |
| author_sort | Gibson, N |
| collection | OXFORD |
| description | Transmission spectroscopy, which consists of measuring the wavelength-dependent absorption of starlight by a planet's atmosphere during a transit, is a powerful probe of atmospheric composition. However, the expected signal is typically orders of magnitude smaller than instrumental systematics and the results are crucially dependent on the treatment of the latter. In this paper, we propose a new method to infer transit parameters in the presence of systematic noise using Gaussian processes, a technique widely used in the machine learning community for Bayesian regression and classification problems. Our method makes use of auxiliary information about the state of the instrument, but does so in a non-parametric manner, without imposing a specific dependence of the systematics on the instrumental parameters, and naturally allows for the correlated nature of the noise. We give an example application of the method to archival NICMOS transmission spectroscopy of the hot Jupiter HD189733, which goes some way towards reconciling the controversy surrounding this data set in the literature. Finally, we provide an appendix giving a general introduction to Gaussian processes for regression, in order to encourage their application to a wider range of problems. © 2011 The Authors Monthly Notices of the Royal Astronomical Society © 2011 RAS. |
| first_indexed | 2024-03-06T23:35:18Z |
| format | Journal article |
| id | oxford-uuid:6d746526-dc00-4120-ac83-2f6ce1e90b58 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T23:35:18Z |
| publishDate | 2012 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:6d746526-dc00-4120-ac83-2f6ce1e90b582022-03-26T19:17:52ZA Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopyJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6d746526-dc00-4120-ac83-2f6ce1e90b58Symplectic Elements at OxfordOxford University Press2012Gibson, NAigrain, SRoberts, SEvans, TOsborne, MPont, FTransmission spectroscopy, which consists of measuring the wavelength-dependent absorption of starlight by a planet's atmosphere during a transit, is a powerful probe of atmospheric composition. However, the expected signal is typically orders of magnitude smaller than instrumental systematics and the results are crucially dependent on the treatment of the latter. In this paper, we propose a new method to infer transit parameters in the presence of systematic noise using Gaussian processes, a technique widely used in the machine learning community for Bayesian regression and classification problems. Our method makes use of auxiliary information about the state of the instrument, but does so in a non-parametric manner, without imposing a specific dependence of the systematics on the instrumental parameters, and naturally allows for the correlated nature of the noise. We give an example application of the method to archival NICMOS transmission spectroscopy of the hot Jupiter HD189733, which goes some way towards reconciling the controversy surrounding this data set in the literature. Finally, we provide an appendix giving a general introduction to Gaussian processes for regression, in order to encourage their application to a wider range of problems. © 2011 The Authors Monthly Notices of the Royal Astronomical Society © 2011 RAS. |
| spellingShingle | Gibson, N Aigrain, S Roberts, S Evans, T Osborne, M Pont, F A Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopy |
| title | A Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopy |
| title_full | A Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopy |
| title_fullStr | A Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopy |
| title_full_unstemmed | A Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopy |
| title_short | A Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopy |
| title_sort | gaussian process framework for modelling instrumental systematics application to transmission spectroscopy |
| work_keys_str_mv | AT gibsonn agaussianprocessframeworkformodellinginstrumentalsystematicsapplicationtotransmissionspectroscopy AT aigrains agaussianprocessframeworkformodellinginstrumentalsystematicsapplicationtotransmissionspectroscopy AT robertss agaussianprocessframeworkformodellinginstrumentalsystematicsapplicationtotransmissionspectroscopy AT evanst agaussianprocessframeworkformodellinginstrumentalsystematicsapplicationtotransmissionspectroscopy AT osbornem agaussianprocessframeworkformodellinginstrumentalsystematicsapplicationtotransmissionspectroscopy AT pontf agaussianprocessframeworkformodellinginstrumentalsystematicsapplicationtotransmissionspectroscopy AT gibsonn gaussianprocessframeworkformodellinginstrumentalsystematicsapplicationtotransmissionspectroscopy AT aigrains gaussianprocessframeworkformodellinginstrumentalsystematicsapplicationtotransmissionspectroscopy AT robertss gaussianprocessframeworkformodellinginstrumentalsystematicsapplicationtotransmissionspectroscopy AT evanst gaussianprocessframeworkformodellinginstrumentalsystematicsapplicationtotransmissionspectroscopy AT osbornem gaussianprocessframeworkformodellinginstrumentalsystematicsapplicationtotransmissionspectroscopy AT pontf gaussianprocessframeworkformodellinginstrumentalsystematicsapplicationtotransmissionspectroscopy |