Abstract
Quasiperiodic oscillations (QPO's) in cataclysmic variables (CV) can be very subtle as well as their confidence of detection, indicating less significance than what the reality is. In our observed object, MV Lyrae, we focus on such QPO's. We simulated the QPO according to Timmer and Koenig (Astron. Astrophys. 300:707–710, 1995) and estimated its confidence intervals. Some known (not obvious) QPO's fell under 1-\(\sigma \) and therefore are not significant regarding this method. We propose and evaluate Support Vector Machine (SVM) models trained to identify those QPO's. Our main goal is to testify whether the accuracy of QPO detection using machine learning methods is higher than the significance of confidence levels obtained by the use of Timmer and Koenig’s (Astron. Astrophys. 300:707–710, 1995) simulations.
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References
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Acknowledgements
The authors gratefully acknowledge the contribution of the Scientific Grant Agency of the Slovak Republic under the VEGA grant 1/0408/20, and the European Regional Development Fund, project No. ITMS2014+: 313011W085.
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Benka, D., Dobrotka, A., Strémy, M. (2023). Detection of Quasi-Periodic Oscillations in Time Series of a Cataclysmic Variable Using Support Vector Machine. In: Bufano, F., Riggi, S., Sciacca, E., Schilliro, F. (eds) Machine Learning for Astrophysics. ML4Astro 2022. Astrophysics and Space Science Proceedings, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-031-34167-0_3
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DOI: https://doi.org/10.1007/978-3-031-34167-0_3
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