Searching for exoplanets using the transit method.
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Date
2017
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Abstract
We present a study designed to detect transiting exoplanets in Kepler
light curve data. We developed an exoplanet detection algorithm based on
modelling transit light curves and fitting the models to light curve data using a chi-square minimization approach in order to identify exoplanets and
estimate their properties such as orbital period, planetary radius and semi-
major axis (orbital radius) from the best t parameters of the model. We
applied our algorithm to a blind sample of Kepler mission data consisting of
approximately 4500 stars. The selection criteria for the blind sample were
Tstar < 6000 K, Rstar < 1R and 13:5 < Kepler Magnitude < 14. The blind
sample contained 70 known exoplanets. Our algorithm detected 50 of the
70 known exoplanets in the blind sample. We found that our algorithm was
effective in detecting exoplanets with planet-star radius ratios greater than
0.01 (k > 0:01) and/or exoplanets with radii greater than 2:5R , as well as
short-period exoplanets (p < 90 days). Twenty four of the exoplanets in the
blind sample were from multi-planetary systems and, in these cases, we found
our algorithm first fits for the largest transit depth and/or (subsequently) for
the shortest orbital period. We did not find any potentially habitable exo-
planets in our blind sample. This is not unexpected as, of more than 3400
exoplanets found to date after surveying upward of 500 000 stars, only 52
exoplanets are considered potentially habitable to varying degrees i.e. 1.5%
of all exoplanets found to date are considered potentially habitable.
Description
Master of Science in Applied Mathematics, University of KwaZulu-Natal, Westville, 2017.