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Identifying Exoplanets and Unmasking False Positives with NGTS

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cam.restrictionthesis_access_open
cam.supervisorQueloz, Didier
cam.thesis.fundingtrue
dc.contributor.authorGünther, Maximilian Norbert
dc.contributor.orcidGünther, Maximilian Norbert [0000-0002-3164-9086]
dc.date.accessioned2018-07-06T08:57:12Z
dc.date.available2018-07-06T08:57:12Z
dc.date.issued2018-07-20
dc.date.submitted2018-03-29
dc.date.updated2018-07-05T16:44:37Z
dc.description.abstractIn my PhD, I advanced the scientific exploration of the Next Generation Transit Survey (NGTS), a ground-based wide-field survey operating at ESO’s Paranal Observatory in Chile since 2016. My original contribution to knowledge is the development of novel methods to 1) estimate NGTS’ yield of planets and false positives; 2) disentangle planets from false positives; and 3) accurately characterise planets. If an exoplanet passes (transits) in front of its host star, we can measure a periodic decrease in brightness. The study of transiting exoplanets gives insight into their size, formation, bulk composition and atmospheric properties. Transit surveys are limited by their ability to identify false positives, which can mimic planets and out-number them by a hundredfold. First, I designed a novel yield simulator to optimise NGTS’ observing strategy and identification of false positives (published in Günther et al., 2017a). This showed that NGTS’ prime targets, Neptune- and Earth-sized signals, are frequently mimicked by blended eclipsing binaries, allowing me to quantify and prepare strategies for candidate vetting and follow-up. Second, I developed a centroiding algorithm for NGTS, achieving a precision of 0.25 milli-pixel in a CCD image (published in Günther et al., 2017b). With this, one can measure a shift of light during an eclipse, readily identifying unresolved blended objects. Third, I innovated a joint Bayesian fitting framework for photometry, centroids, and radial velocity cross-correlation function profiles. This allows to disentangle which object (target or blend) is causing the signal and to characterise the system. My method has already unmasked numerous false positives. Most importantly, I confirmed that a signal which was almost erroneously rejected, is in fact an exoplanet (published in Günther et al., 2018). The presented achievements minimise the contamination with blended false positives in NGTS candidates by 80%, and show a new approach for unmasking hidden exoplanets. This research enhanced the success of NGTS, and can provide guidance for future missions.
dc.description.sponsorshipThroughout my PhD, I have been supported by the UK Science and Technology Facilities Council (STFC) award reference 1490409 as well as an Isaac Newton Studentship.
dc.identifier.doi10.17863/CAM.25208
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/277873
dc.language.isoen
dc.publisher.collegeGirton College
dc.publisher.departmentDepartment of Physics
dc.publisher.institutionUniversity of Cambridge
dc.rightsAll rights reserved
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.subjectastrophysics
dc.subjectastronomy
dc.subjectexoplanet
dc.subjectplanet
dc.subjectsurvey
dc.subjecttransit
dc.subjecteclipse
dc.subjectoccultation
dc.subjecteclipsing binary
dc.subjectfalse positive
dc.subjectvetting
dc.subjectBayes
dc.subjectBayesian
dc.subjectMarkov Chain Monte Carlo
dc.subjectMCMC
dc.subjectGaussian Process
dc.subjectNext Generation Transit Survey
dc.subjectNGTS
dc.subjectyield
dc.subjectcentroiding
dc.subjectNGTS-3
dc.subjectNGTS-3Ab
dc.titleIdentifying Exoplanets and Unmasking False Positives with NGTS
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.type.qualificationtitlePhD

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