Fast folding algorithm

<p>The Fast-Folding Algorithm (FFA) is a computational method primarily utilized in the domain of astronomy for detecting periodic signals. 
FFA is designed to reveal repeating or cyclical patterns by "folding" data, which involves dividing the data set into numerous segments, aligning these segments to a common phase, and summing them together to enhance the signal of periodic events. This <a href="/facts/Algorithm/fnl5NmRt">algorithm</a> is particularly advantageous when dealing with non-uniformly sampled data or signals with a drifting period, which refer to signals that exhibit a frequency or period drifting over space and time, such cycles are not stable and consistent; rather, they are randomized.
A quintessential application of FFA is in the detection and analysis of <a href="/facts/Pulsar/f8c7S5LQ">pulsars</a>—highly magnetized, rotating neutron stars that emit beams of <a href="/facts/Electromagnetic_radiation/Ynx7ujCW">electromagnetic radiation</a>. By employing FFA, astronomers can effectively distinguish noisy data to identify the regular pulses of radiation emitted by these celestial bodies. Moreover, the Fast-Folding Algorithm is instrumental in detecting long-period signals, which is often a challenge for other algorithms like the <a href="/facts/Fast_Fourier_transform/caT7UUCh">FFT</a> (Fast-Fourier Transform) that operate under the assumption of a constant frequency. Through the process of folding and summing data segments, FFA provides a robust mechanism for unveiling periodicities despite noisy observational data, thereby playing a pivotal role in advancing our understanding of pulsar properties and behaviors.
</p>

Fast folding algorithm open-in-new

Fast folding algorithm