A Method for the Detection of Planetary Transits in Large Time Series Data Sets

We present a fast, efficient and easy to apply computational method for thedetection of planetary transits in large photometric datasets. The code hasbeen specifically produced to analyse an ensemble of 21,950 stars in theglobular cluster 47 Tucanae, the results of which are the subject of a separatepaper. Using cross correlation techniques and Monte Carlo tested detectioncriteria, each time-series is compared with a large database of appropriatetransit models. The algorithm recovers transit signatures with high efficiencywhile maintaining a low false detection probability, even in noisy data. This is illustrated by describing its application to our 47 Tuc dataset, forwhich the algorithm produced a weighted mean transit recoverabilty spanning 85%to 25% for orbital periods of 1-16 days despite gaps in the time series causedby weather and observing duty cycle. The code is easily adaptable and iscurrently designed to accept time-series produced using Difference ImagingAnalysis.