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Title: Identifying exoplanets and unmasking false positives with NGTS
Author: Günther, Maximilian Norbert
ISNI:       0000 0004 7426 4316
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2018
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In 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.
Supervisor: Queloz, Didier Sponsor: Science and Technology Facilities Council (STFC) ; Isaac Newton Studentship
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: astrophysics ; astronomy ; exoplanet ; planet ; survey ; transit ; eclipse ; occultation ; eclipsing binary ; false positive ; vetting ; Bayes ; Bayesian ; Markov Chain Monte Carlo ; MCMC ; Gaussian Process ; Next Generation Transit Survey ; NGTS ; yield ; centroiding ; NGTS-3 ; NGTS-3Ab