Sparse Box-fitting Least Squares

Abstract

We present a new implementation of the commonly used Box-fitting Least Squares (BLS) algorithm, for the detection of transiting exoplanets in photometric data. Unlike BLS, our new implementation - Sparse BLS (SBLS), does not use binning of the data into phase bins, nor does it use any kind of phase grid. Thus, its detection efficiency does not depend on the transit phase, and is therefore slightly better than that of BLS. For sparse data, it is also significantly faster than BLS. It is therefore perfectly suitable for large photometric surveys producing unevenly-sampled sparse light curves, such as Gaia.

Published by PASP, 6 pages, 6 figures

References in corpus (4)

• The Gaia mission
• Transit Least Squares: Optimized transit detection algorithm to search for periodic transits of small planets
• A fast hybrid algorithm for exoplanetary transit searches
• The BAST algorithm for transit detection

Cited by in corpus (6)

• Gaia Data Release 3. Summary of the variability processing and analysis
• A Systematic Search for Short-period Close White Dwarf Binary Candidates Based on Gaia EDR3 Catalog and Zwicky Transient Facility Data
• The Detection of Transiting Exoplanets by Gaia
• fBLS -- a fast-folding BLS algorithm
• The GPU Phase Folding and Deep Learning Method for Detecting Exoplanet Transits
• Adaptation of the Phase Distance Correlation Periodogram to Account for Measurement Uncertainties