CITATION.cff

cff-version: 1.2.0
message: "If you use cuPeriod in your research, please cite the Research Note below."
title: cuPeriod
abstract: >-
  Optimized, GPU-accelerated periodograms for astronomy: eight period-search methods (GLS,
  BLS, PDM, CE, String-Length, MHAOV, TLS, SuperSmoother) with fast CPU and CUDA backends,
  scaling from a single light curve to millions. A portable PyTorch backend extends GPU
  acceleration beyond NVIDIA to AMD (ROCm), Intel (XPU), and Apple (MPS) hardware, and a
  multi-threaded numba CPU tier gives every method a fast backend even without a GPU. An
  optional PySide6 desktop GUI provides an interactive periodogram explorer over the same
  API. For classical pulsators it also performs automated, uncertainty-aware
  pre-whitening: iterative sinusoid extraction with principled stopping criteria, error
  propagation, combination-frequency identification, and g-mode period-spacing tools.
type: software
authors:
  - given-names: Tharindu
    family-names: Jayasinghe
    # Add your ORCID here to make the citation unambiguous, e.g.:
    # orcid: "https://orcid.org/0000-0000-0000-0000"
version: 1.2.0
date-released: "2026-08-30"
license: GPL-3.0-or-later
repository-code: "https://github.com/tjayasinghe/cuPeriod"
url: "https://cuperiod.readthedocs.io"
keywords:
  - astronomy
  - periodogram
  - lomb-scargle
  - box-least-squares
  - variable-stars
  - time-series
  - gpu
  - cuda

# Cite the peer-reviewed Research Note in preference to the software record above;
# GitHub, Zenodo and cffconvert all honour `preferred-citation`.
preferred-citation:
  type: article
  title: "cuPeriod: Seven Validated Periodograms for CPUs and Any GPU"
  authors:
    - given-names: Tharindu
      family-names: Jayasinghe
  journal: "Research Notes of the American Astronomical Society"
  year: 2026
  month: 8
  volume: 10
  issue: 8
  start: 244
  doi: "10.3847/2515-5172/ae9b06"
  url: "https://ui.adsabs.harvard.edu/abs/2026RNAAS..10..244J"