Denis Benka*, Sabína Vašová, Michal Kebísek, Maximilián Strémy
Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2519-2535, 2023, DOI:10.32604/iasc.2023.040799
Abstract Pattern recognition algorithms are commonly utilized to discover certain patterns, particularly in image-based data. Our study focuses on quasiperiodic oscillations (QPO) in celestial objects referred to as cataclysmic variables (CV). We are dealing with interestingly indistinct QPO signals, which we analyze using a power density spectrum (PDS). The confidence in detecting the latter using certain statistical approaches may come out with less significance than the truth. We work with real and simulated QPO data of a CV called MV Lyrae. Our primary statistical tool for determining confidence levels is sigma intervals. The aforementioned CV has scientifically proven QPO existence, but… More >
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