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Detecting Gravitational Waves using Constant-Q Transform and Convolutional Neural Networks

Published: 11 April 2022 Publication History

Abstract

The discovery of gravitational waves from the mergers of binary black holes has opened doors to an unprecedented revolution in the fields of physics and astronomy. However, the signals of gravitational waves with tiny magnitudes are inevitably buried in detector noise, leading to a great demand for the accurate analysis of gravitational wave data. In this paper, based on a set of time-series data containing simulated gravitational waves in a Kaggle competition, we propose a deep learning method by combining constant-Q transform and convolutional neural networks (CNNs), to achieve a promising performance for the detection of gravitational waves. In our method, the gravitational wave signal is firstly transformed into a spectrogram by the constant-Q transform, and is subsequently classified by the CNN network. In particular, EfficientNet-B3 and EfficientNetV2-L are both utilized as the CNN backbones to extract features from spectrograms. After applying an ensemble average of two backbones and the K-Fold cross validation technique, our model reaches an AUC score 0.8786 on the private test set. This result ranks top 5% (63/1219) in the Kaggle leaderboard, and can get a bronze medal in the G2Net Gravitational Wave Detection competition. This work would help increase the sensitivity of interferometers to gravitational wave signals, and potentially accelerate the development of next-generation detectors to explore the Universe.

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Cited By

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  • (2023)Optimized Detection of Continuous Gravitational-Wave Signals using Convolutional Neural Network2023 3rd International conference on Artificial Intelligence and Signal Processing (AISP)10.1109/AISP57993.2023.10134809(1-5)Online publication date: 18-Mar-2023
  • (2022)Dual-valued Neural Networks2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)10.1109/AVSS56176.2022.9959227(1-8)Online publication date: 29-Nov-2022

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      cover image ACM Other conferences
      CIIS '21: Proceedings of the 2021 4th International Conference on Computational Intelligence and Intelligent Systems
      November 2021
      95 pages
      ISBN:9781450385930
      DOI:10.1145/3507623
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 11 April 2022

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      Author Tags

      1. Constant-Q transform
      2. Convolutional neural network
      3. Deep learning
      4. EfficientNet
      5. Gravitational wave

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      • (2023)Optimized Detection of Continuous Gravitational-Wave Signals using Convolutional Neural Network2023 3rd International conference on Artificial Intelligence and Signal Processing (AISP)10.1109/AISP57993.2023.10134809(1-5)Online publication date: 18-Mar-2023
      • (2022)Dual-valued Neural Networks2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)10.1109/AVSS56176.2022.9959227(1-8)Online publication date: 29-Nov-2022

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