arXiv Machine Learning
Survey of Novel Deep Learning Architectures for Denoising Gravitational-wave Signals
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Flexible Gravitational-Wave Parameter Estimation with Transformers
arXiv:2512. 02968v2 Announce Type: replace-cross Abstract: Gravitational-wave data analysis relies on accurate and efficient methods to extract physical information from noisy detector signals, yet the increasing rate and complexity of observations represent a growing challenge.
arXiv Machine Learning
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Deep Learning Detection of Beyond-General-Relativity Deviations in Gravitational-Wave Signals: A Detection-Threshold Study with Real LIGO Noise
The paper investigates machine‑learning detection of controlled deviations from General Relativity in gravitational‑wave signals. Using a hybrid classifier that combines a one‑dimensional convolutional neural network with ten hand‑crafted waveform statistics, the authors train on General‑Relativistic and modified waveforms and test on a deviation type not seen during training. They find a detection threshold at a dimensionless strength coefficient β ≈ 0.25 when using real GW150914 strain and real H1 detector noise, with accuracy improving from chance at β ≤ 0.2 to perfect classification at β ≥ 0.5.
arXiv Machine Learning
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Binary Black Hole Parameter Estimation with Hybrid CNN-Transformer Neural Networks
arXiv:2606. 13941v1 Announce Type: cross Abstract: The detection of gravitational waves has revolutionized our ability to explore fundamental aspects of the Universe.
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arXiv AI
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AI-enabled gravitational-waves searches for binary neutron stars at optimal sensitivity
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arXiv Machine Learning
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Improving the Sensitivity of Gravitational Wave Detection with Weighted Conformal Prediction
The paper proposes a weighted conformal prediction framework to combine outputs from multiple gravitational‑wave search pipelines, providing statistically rigorous confidence estimates for candidate events. By incorporating likelihood‑ratio reweighting, the method corrects for covariate shift between simulated training data and real observations, restoring well‑calibrated coverage. Experiments on mock datasets show that this approach improves sensitivity, especially near the detection threshold, enabling recovery of true signals that would otherwise be missed.