GERBLS—A Rapid Box Least Squares Algorithm for Exoplanet Detection That Scales Well for Large Datasets
- 1Penn State
- 2Department of Astrophysical Sciences, Princeton University, 4 Ivy Lane, Princeton, NJ 08544, USA
- 3University of Florida
Abstract
Box least squares (BLS) is a robust but computationally expensive method to search for transits in photometric data. Reducing its computational cost is especially relevant when scanning large datasets or conducting transit injection and recovery simulations. Greatly Expedited Revamp of BLS (GERBLS) is a software algorithm designed to substantially speed up a BLS search. It offers O ( n log n ) runtime scaling as opposed to O ( n ² ) for native brute-force BLS. GERBLS is based on a divide-and-conquer FFA that minimizes mathematically redundant operations during phase-folding. It can automatically downsample the input data at longer tested periods and yield a BLS period resolution that is close to optimal for transiting planets on circular orbits. GERBLS typically outperforms an efficient CPU-based brute-force BLS implementation by at least 10–100 times in total runtime, with a relative speedup of 600× demonstrated for a dataset consisting of 10 ⁵ observations.
Related
- Exoplanet detection through photometric orbital phase modulations
- Exoplanet Detection Using Adaptive Quantum-Optimal Measurement
- On-sky performance and prospects of near-infrared CMOS detectors in exoplanet detection: first year of SPIRIT
- Molecular mapping of an exoplanet with JWST: NH3 detection in the temperate super-Jupiter Epsilon Indi Ab
- Stellar flares with Ariel: detection prospects and impacts on exoplanet spectroscopy
BibTeX
@article{ment2026gerblsa,
title = {{GERBLS—A Rapid Box Least Squares Algorithm for Exoplanet Detection That Scales Well for Large Datasets}},
author = {Kristo Ment and Eric B. Ford and Suvrath Mahadevan and Rachel B. Fernandes and Andrew Hotnisky},
journal = {The Astronomical Journal},
year = {2026},
month = oct,
doi = {10.3847/1538-3881/aea0c8},
url = {https://iopscience.iop.org/article/10.3847/1538-3881/aea0c8},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - GERBLS—A Rapid Box Least Squares Algorithm for Exoplanet Detection That Scales Well for Large Datasets
AU - Kristo Ment
AU - Eric B. Ford
AU - Suvrath Mahadevan
AU - Rachel B. Fernandes
AU - Andrew Hotnisky
PY - 2026
DA - 2026/10/06
JO - The Astronomical Journal
DO - 10.3847/1538-3881/aea0c8
UR - https://iopscience.iop.org/article/10.3847/1538-3881/aea0c8
AB - Box least squares (BLS) is a robust but computationally expensive method to search for transits in photometric data. Reducing its computational cost is especially relevant when scanning large datasets or conducting transit injection and recovery simulations. Greatly Expedited Revamp of BLS (GERBLS) is a software algorithm designed to substantially speed up a BLS search. It offers O ( n log n ) runtime scaling as opposed to O ( n ² ) for native brute-force BLS. GERBLS is based on a divide-and-conquer FFA that minimizes mathematically redundant operations during phase-folding. It can automatically downsample the input data at longer tested periods and yield a BLS period resolution that is close to optimal for transiting planets on circular orbits. GERBLS typically outperforms an efficient CPU-based brute-force BLS implementation by at least 10–100 times in total runtime, with a relative speedup of 600× demonstrated for a dataset consisting of 10 ⁵ observations.
SN - 0004-6256
SN - 1538-3881
ER -