Papers › CETRA: A fast, sensitive exoplanet transit detection algorithm implemented for GPUs
CETRA: A fast, sensitive exoplanet transit detection algorithm implemented for GPUs
Leigh C. Smith, Saad Ahmed, Francesca De Angeli, P. W. Burgess, Giorgia Busso, Dominic C. Ford, Diana L. Harrison, S. T. Hodgkin, Jonathan M. Irwin, Guy T. Rixon, Nicholas A. Walton
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
We present the Cambridge Exoplanet Transit Recovery Algorithm (CETRA), a fast and sensitive transit detection algorithm, optimised for GPUs. CETRA separates the task into a search for transit signals across linear time space, followed by a phase-folding of the former to enable a periodic signal search, using a physically motivated transit model to improve detection sensitivity. It outperforms traditional methods like Box Least Squares and Transit Least Squares in both sensitivity and speed. Tests on synthetic light curves demonstrate that CETRA can identify at least 20 per cent more low-SNR transits than Transit Least Squares in the same data, particularly those of long period planets. It is also shown to be up to a few orders of magnitude faster for high cadence light curves, enabling rapid large-scale searches. Through application of CETRA to Transiting Exoplanet Survey Satellite short cadence data, we recover the three planets in the HD 101581 system with improved significance. In particular, the transit signal of the previously unvalidated planet TOI-6276.03 is enhanced from SNR=7.9 to SNR=16.0, which means it may now meet the criteria for statistical validation. CETRA's speed and sensitivity make it well-suited for current and future exoplanet surveys, particularly in the search for Earth analogues. Our implementation of this algorithm uses NVIDIA's CUDA platform and requires an NVIDIA GPU, it is open-source and available from GitHub and PyPI.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections