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

作     者：Denis Benka Sabína Vasová Michal Kebísek Maximilián Strémy 

作者机构：Institute of Applied InformaticsAutomation and MechatronicsFaculty of Materials Science and TechnologySlovak University of TechnologyTrnava91724Slovakia 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第37卷第9期

页      面：2519-2535页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Convolutional neural network quasi-periodic oscillation cataclysmic variable simulation 

摘      要：Pattern recognition algorithms are commonly utilized to discover certain patterns,particularly in image-based *** 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 *** work with real and simulated QPO data of a CV called MV *** primary statistical tool for determining confidence levels is sigma *** 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 *** intend to propose and ultimately train a convolutional neural network(CNN)using two types of QPO data with varying amounts of training dataset *** aim to demonstrate the accuracy and viability of the classification using a CNN in comparison to sigma *** resulting detection rate of our algorithm is very plausible,thus proving the effectiveness of CNNs in this scientific area.