Optimizing the search for transiting planets in long time series
Aviv Ofir

TL;DR
This paper introduces an optimized method for detecting transiting exoplanets in long-term light curves by leveraging Keplerian dynamics, resulting in increased sensitivity and reduced computational costs.
Contribution
The paper develops the Optimal BLS algorithm, which improves transit detection efficiency by incorporating system parameters and non-linear frequency sampling.
Findings
Optimal BLS enhances detection of short and long period planets.
Cubic frequency sampling outperforms linear sampling for transit detection.
Significant reduction in computational resources needed for long-term datasets.
Abstract
Context: Transit surveys, both ground- and space- based, have already accumulated a large number of light curves that span several years. Aims: The search for transiting planets in these long time series is computationally intensive. We wish to optimize the search for both detection and computational efficiencies. Methods: We assume that the searched systems can be well described by Keplerian orbits. We then propagate the effects of different system parameters to the detection parameters. Results: We show that the frequency information content of the light curve is primarily determined by the duty cycle of the transit signal, and thus the optimal frequency sampling is found to be cubic and not linear. Further optimization is achieved by considering duty-cycle dependent binning of the phased light curve. By using the (standard) BLS one is either rather insensitive to long-period planets,…
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