Other research projects
Unmasking the hidden NGTS-3Ab
NGTS-3 is a very tricky system: at first it looked like a 'simple' Hot Jupiter around a Sun-like star. After taking some radial velocity measurements, it suddenly appeared that there is some 'funkyness' in the data. So it was nearly disregarded as a false positive. After extensive analysis, however, we could save this happy big planet: it turned out to be a Hot Jupiter in a previously (and still visually) unresolved binary star system.
Identifying false positives by measuring stellar light to 0.00025 image pixel
Astrophysical false positives are crucial to avoid in a wide-field transit survey like NGTS. Eclipsing binaries with a low-mass companion or grazing eclipses, as well as diluted eclipsing binaries or variable stars in the background can mimic a planet transit and be very costly to follow up. They can best be identified with the centroiding technique: if the photometric flux is lost off-centre during an eclipse, the flux centroid shifts towards the centre of the target star. Although this method has successfully been employed by the Kepler mission, it has previously not been proven feasible from the ground. A new algorithm I developed allows NGTS to detect centroid shifts with a precision of down to 0.00025 pixel. This makes NGTS the first ground-based wide-field transit survey ever to successfully implement this technique for candidate auto-vetting.
Optimizing the yield of transit surveys
YETI (Yield Estimator for Transit Instruments) is a Python-based simulator I developed to estimate the yield of planets and false positives for transit surveys like NGTS, Kepler and TESS. It enables to evaluate the influence of field selection, observing strategies, noise properties, detection criteria and false alarm rates. Further, with YETI one can diagnose methods for candidate vetting, and establish appropriate follow-up strategies.