Detecting multiple periodicities in observational data with the multi-frequency periodogram. II. Frequency Decomposer, a parallelized time-series analysis algorithm
Central Astronomical Observatory at Pulkovo of Russian Academy of Sciences, Pulkovskoje sh. 65, St Petersburg 196140, Russia
arXiv:1309.0100 [astro-ph.IM], (31 Aug 2013)
@article{2013arXiv1309.0100B,
author={Baluev}, R.~V.},
title={"{Detecting multiple periodicities in observational data with the multi-frequency periodogram. II. Frequency Decomposer, a parallelized time-series analysis algorithm}"},
journal={ArXiv e-prints},
archivePrefix={"arXiv"},
eprint={1309.0100},
primaryClass={"astro-ph.IM"},
keywords={Astrophysics – Instrumentation and Methods for Astrophysics, Astrophysics – Solar and Stellar Astrophysics},
year={2013},
month={aug},
adsurl={http://adsabs.harvard.edu/abs/2013arXiv1309.0100B},
adsnote={Provided by the SAO/NASA Astrophysics Data System}
}
This is a parallelized algorithm performing a decomposition of a noisy time series into a number of frequency components. The algorithm analyses all suspicious periodicities that can be revealed, including the ones that look like an alias or noise at a glance, but later may prove to be a real variation. After selection of the initial candidates, the algorithm performs a complete pass through all their possible combinations and computes the rigorous multi-frequency statistical significance for each such frequency tuple. The largest combinations that still survived this thresholding procedure represent the outcome of the analysis. The parallel computing on a graphics processing unit (GPU) is implemented through CUDA and brings a significant performance increase. It is still possible to run FREDEC solely on CPU in the traditional single-threaded mode, when no suitable GPU device is available.
September 4, 2013 by hgpu
