Abstract
Machine learning methods are widely used to identify specific patterns, especially in image based data. In our research we focus on quasiperiodic oscillations (QPO) in astronomical objects known as cataclysmic variables (CV). We work with very subtle QPO signals in the form of a power density spectrum (PDS). The confidence of detection of the latter using some common statistical methods could yield less significance than the reality is. We work with real and simulated QPO data and we use sigma intervals as our main statistical method to get the confidence levels. As expected, most of our observed QPO fell under 1-\(\sigma \) and based off this method, such QPO is not significant. In our work, we would like to propose and subsequently evaluate two machine learning algorithms with different lengths of training data. Our main goal is to testify the accuracy and feasibility of the selected machine learning methods in contrast to the sigma intervals. The aim of this paper is to summarise both the theory needed to understand the problem and the results of our conducted research.
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Benka, D., Vašová, S., Kebísek, M., Strémy, M. (2023). Detection of Variable Astrophysical Signal Using Selected Machine Learning Methods. In: Silhavy, R., Silhavy, P. (eds) Artificial Intelligence Application in Networks and Systems. CSOC 2023. Lecture Notes in Networks and Systems, vol 724. Springer, Cham. https://doi.org/10.1007/978-3-031-35314-7_57
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