AI Insight
This study demonstrates that convolutional neural networks can effectively classify gravitational wave signals from spinning binary black hole mergers and constrain their parameter space to reduce computational costs in matched-filter searches. The researchers achieved over 99.8% accuracy in distinguishing signals from noise using only template bank training data, and found that chirp mass-duration representation yielded the highest accuracy (93.2%) for identifying the correct parameter region among five tested approaches. The work extends previous methods from non-spinning to aligned-spin binary black hole systems, showing that parameter-space representation choice critically affects signal parameter constraint accuracy.
Why it matters
This approach could significantly reduce the computational burden of gravitational wave detection by narrowing the search space before applying computationally expensive matched-filtering techniques. The findings provide practical guidance for implementing neural network-assisted gravitational wave searches in operational detectors like LIGO, potentially enabling faster detection and analysis of binary black hole mergers.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: The detection of gravitational waves (GWs) from compact binary coalescences (CBCs) using matched filtering is computationally demanding because detector data must be correlated with many template waveforms spanning a high-dimensional intrinsic parameter space. In our previous work, we showed that a convolutional neural network (CNN) can classify noisy signals against pure noise and constrain the intrinsic parameter space of a true non-spinning binary black hole (BBH) signal, enabling a narrower matched-filter search region and reducing computational cost. Here, we extend this framework to aligned-spin BBH systems and investigate how different parameter-space representations affect CNN-based patch identification. Using IMRPhenomD waveforms over the aligned-spin BBH parameter space and Advanced LIGO design sensitivity, we show that a CNN trained solely on the aligned-spin template bank achieves more than 99.8% signal-noise classification accuracy on independently generated uniformly sampled BBH signals, indicating that additional uniformly sampled training data are unnecessary. We partition the template bank into four approximately balanced patches using a Principal Component Analysis (PCA)-based quantile scheme and evaluate five parameter-space representations for patch identification. The chirp mass-duration representation achieves the highest average accuracy (93.2%), followed by chirp mass (91.6%) and component masses (90.5%). The post-Newtonian coordinates tau_0-tau_3 and theta_0-theta_3-theta_3s yield substantially lower accuracies. These results show that the existing template bank is sufficient for training and high-accuracy signal detection, while the choice of parameter-space representation is critical for constraining the parameters of the true signal.