Detection of a Quasiperiodic Phenomenon of a Binary Star System Using Convolutional Neural Network

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 as indicated by our analysis,the QPO ended up falling below 1-σ,and such QPOs are not noteworthy based on the former approach.We intend to propose and ultimately train a convolutional neural network(CNN)using two types of QPO data with varying amounts of training dataset lengths.We aim to demonstrate the accuracy and viability of the classification using a CNN in comparison to sigma intervals.The resulting detection rate of our algorithm is very plausible,thus proving the effectiveness of CNNs in this scientific area.