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GERBLS—A Rapid Box Least Squares Algorithm for Exoplanet Detection That Scales Well for Large Datasets

  • Authors: Kristo Ment, Eric B. Ford, Suvrath Mahadevan, Rachel B. Fernandes, Andrew Hotnisky

Kristo Ment et al 2026 The Astronomical Journal 172 .

  • Provider: AAS Journals

Caption: Figure 6.

Top row: performance of AstroPy BLS in recovering injected box-shaped signals. In total, AstroPy BLS recovered 83.8% of all injected signals. Bottom row: performance of GERBLS (without automatic downsampling) in recovering the same injected box-shaped signals. In total, GERBLS recovered 84.4% of all injected signals. When turning on downsampling, the recovery percentage rises to 84.9%. Left column: histogram of the S/N ratios of all injected signals (red line), sampled uniformly between 4 and 10, and histogram of those injected signals that were successfully recovered by the BLS algorithm (solid blue). The fraction of recovered signals drops rapidly for S/N values below 6. Middle column: distribution of the S/N values recorded by the BLS algorithm during recovery, versus the initial injected S/N values of these same box-shaped signals. Only successfully recovered signals are included. The recovered S/N distribution plateaus close to S/N ≈ 6, representing an empirical detection limit. Right column: same as the middle column, except transit depths are plotted instead of the corresponding S/N values.

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