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The detection of gravitational waves events in the data taken by the experiments LIGO, Virgo and KAGRA requires an extensive computing process to extract the signals from the noise. The traditional technique to perform this task is matched filtering. Although powerful, it is computationally very expensive and new techniques are being studied. In this chapter, the use of convolutional neural networks to detect signals of compact binary coalescences using two-dimensional spectrograms as input is explained. Different neural networks are used for different mass ranges. The results of the searches in O3 data are shown.

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Using Convolutional Neural Networks to Search Gravitational Wave Events in LIGO-Virgo-KAGRA Data

  • 章节

  • 发表日期：

    2025年

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  • Marc Andrés-Carcasona,
  • Alexis Menéndez-Vázquez,
  • Mario Martínez,
  • Lluïsa-Maria Mir

摘要

The detection of gravitational waves events in the data taken by the experiments LIGO, Virgo and KAGRA requires an extensive computing process to extract the signals from the noise. The traditional technique to perform this task is matched filtering. Although powerful, it is computationally very expensive and new techniques are being studied. In this chapter, the use of convolutional neural networks to detect signals of compact binary coalescences using two-dimensional spectrograms as input is explained. Different neural networks are used for different mass ranges. The results of the searches in O3 data are shown.