A Gaussian process framework for modelling stellar activity signals in radial velocity data
- Submitting institution
-
University of Oxford
- Unit of assessment
- 12 - Engineering
- Output identifier
- 9387
- Type
- D - Journal article
- DOI
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10.1093/mnras/stv1428
- Title of journal
- MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
- Article number
- -
- First page
- 2269
- Volume
- 452
- Issue
- 3
- ISSN
- 0035-8711
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper culminates a tranche of work bringing Bayesian non-parametrics to exoplanet studies. It extends the 2012 approach (which established NASA Kepler light curve detrending pipeline) achieving superior results in “radial velocity” exoplanet detection using probabilistic models. The latter is vital for new generation of instruments (TESS, PLATO). A follow-up paper “Ghost in the time series: no planet for Alpha Cen B” (10.1093/mnrasl/slv164), demonstrated no planet existed around a nearby star. Gaussian Processes in exoplanet studies have been widely adopted. Open Software Repositories of the code are widely accessed and the approaches have been incorporated into several analyses and pipelines.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -