The discovery and characterization of extrasolar planets is a leading frontier of science, which is limited by our ability to extract real astrophysical signals masked by systematic noise. In this Thesis I develop techniques for self-calibration in optical astronomy, in both imaging and photometry, applicable both to observations from the ground and for space telescopes, and apply these to searching for exoplanets. Kernel phase interferometry is a method for improving high angular resolution astronomical imaging by cancelling out the effects of the turbulent atmosphere. I derive a generalization, kernel amplitude, to correct also for the effects of scintillation, or twinkling. I go on to demonstrate kernel phase for the first time from the ground using the Palomar Hale 200-Inch Telescope, as a test case for the extreme adaptive optics instruments SPHERE and GPI. The Kepler satellite and its successor, the K2 mission, have been crucial to our understanding both of exoplanets, and via asteroseismology, of stellar physics. The success of K2 depends on correcting for its unstable pointing. Using semi-parametric Bayesian statistical models to overcome this issue, I report the discovery of 145 new transiting planet candidates in data from Campaigns 5 and 6 of the K2 mission. Furthermore, with two novel techniques, 'smear' and 'halo' photometry, which dramatically extend the dynamic range of Kepler and K2, I recover light curves of bright stars that were previously too saturated to study, and definitively detect variability in the seven naked-eye stars in the Pleiades cluster. These new data analysis approaches enable the K2 and TESS space missions to discover planets transiting the nearest and brightest stars, which will be ideal targets for the coming era of exoplanet characterization.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:740942 |
| Date | January 2017 |
| Creators | Pope, Benjamin James Spinks |
| Contributors | Aigrain, Suzanne |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://ora.ox.ac.uk/objects/uuid:d692bf96-ccf8-47bf-b246-1bbedcce60e5 |
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