This is a parallelized algorithm performing a decomposition of the time-series data into a number of sinusoidal components, disentangling them from the white Gaussian noise. The algorithm analyses all suspicious frequencies, including the ones that look like an alias at a glance, but may become preferable later. After selection of the initial frequency candidates, the algorithm passes through all their possible combinations and estimates their multi-frequency statistical significance. In the end, it prints out the set of largest frequency tuples that were still found significant.
The GPU computing is implemented through CUDA and brings a significant performance increase. It is still possible to run FreDec solely on CPU, if no suitable GPU device is available in the system.
See the details of the underlying theory in
Baluev 2013, MNRAS, V. 436, P. 807
The description of the algorithm itself can be found in arXiv:1309.0100.
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