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Results: 6
Number of items: 6
  • Open Access
    Mesarcik, M. B. (2024). Machine learning-based anomaly detection for radio telescopes. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Mesarcik, M., Boonstra, A. J., Iacobelli, M., de Laat, C. T. A. M., & van Nieuwpoort, R. V. (2023). The ROAD to discovery: Machine-learning-driven anomaly detection in radio astronomy spectrograms. Astronomy and Astrophysics, 680, Article A74. https://doi.org/10.1051/0004-6361/202347182
  • Open Access
    Mesarcik, M., Ranguelova, E., Boonstra, A.-J., & van Nieuwpoort, R. V. (2022). Improving novelty detection using the reconstructions of nearest neighbours. Array, 14, Article 100182. https://doi.org/10.1016/j.array.2022.100182
  • Open Access
    Mesarcik, M., Boonstra, A.-J., Ranguelova, E., & van Nieuwpoort, R. V. (2022). Learning to detect radio frequency interference in radio astronomy without seeing it. Monthly Notices of the Royal Astronomical Society, 516(4), 5367-5378. https://doi.org/10.1093/mnras/stac2503
  • Mesarcik, M., Boonstra, A.-J., Meijer, C., Jansen, W., Ranguelova, E., & van Nieuwpoort, R. V. (2020). LOFAR dataset for deep learning assisted data Inspection for radio astronomy [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3702430
  • Open Access
    Mesarcik, M., Boonstra, A.-J., Meijer, C., Jansen, W., Ranguelova, E., & van Nieuwpoort, R. V. (2020). Deep learning assisted data inspection for radio astronomy. Monthly Notices of the Royal Astronomical Society, 496(2), 1517-1529. https://doi.org/10.1093/mnras/staa1412
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