TRUFAS, a wavelet based algorithm for the rapid detection of planetary transits

TL;DR

TRUFAS is a fast, robust wavelet-based algorithm designed for automatic detection of planetary transits in large-scale light curve data, particularly suited for space mission datasets like CoRoT.

Contribution

The paper introduces TRUFAS, a novel wavelet-based method that efficiently detects planetary transits with minimal sensitivity to preprocessing variations.

Findings

Successfully identified transits in simulated CoRoT data

Robust against different light curve preprocessing methods

Effective with at least three transit events present

Abstract

Aims: We describe a fast, robust and automatic detection algorithm, TRUFAS, and apply it to data that are being expected from the CoRoT mission. Methods: The procedure proposed for the detection of planetary transits in light curves works in two steps: 1) a continuous wavelet transformation of the detrended light curve with posterior selection of the optimum scale for transit detection, and 2) a period search in that selected wavelet transformation. The detrending of the light curves are based on Fourier filtering or a discrete wavelet transformation. TRUFAS requires the presence of at least 3 transit events in the data. Results: The proposed algorithm is shown to identify reliably and quickly the transits that had been included in a standard set of 999 light curves that simulate CoRoT data. Variations in the pre-processing of the light curves and in the selection of the scale of the wavelet transform have only little effect on TRUFAS' results. Conclusions: TRUFAS is a robust and quick transit detection algorithm, especially well suited for the analysis of very large volumes of data from space or ground-based experiments, with long enough durations for the target-planets to produce multiple transit events.