TSARDI: a Machine Learning data rejection algorithm for transiting exoplanet light curves. Issue 2 (4th September 2018) - Mislis, D; Pyrzas, S; Alsubai, K A
ABSTRACT: We present TSARDI, an efficient rejection algorithm designed to improve the transit detection efficiency in data collected by large-scale surveys. TSARDI is based on the Machine Learning clustering algorithm DBSCAN, and its purpose is to serve as a robust and adaptable filter aiming to identify unwanted noise points left over from data detrending processes. TSARDI is an unsupervised method, which can treat each light curve individually; there is no need of previous knowledge of any other field light curves. We conduct a simulated transit search by injecting planets on real data obtained by the QES project and show that TSARDI leads to an overall transit detection efficiency increase of ∼11 per cent, compared to results obtained from the same sample, but using a standard sigma-clip algorithm. For the brighter end of our sample (host star magnitude <12), TSARDI achieves a detection efficiency of ∼80 per cent of injected planets. While our algorithm has been developed primarily for the field of exoplanets, it is easily adaptable and extendable for use in any time series.