arXiv Machine Learning
Parameter Estimation of Ringdown Quasinormal Modes with Autoencoder
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arXiv Machine Learning
Sep 15
Survey of Novel Deep Learning Architectures for Denoising Gravitational-wave Signals
arXiv:2609.13272v1 Announce Type: cross Abstract: Gravitational-wave denoising must handle the full diversity of spinning, precessing binaries, since the recovered waveform underpins parameter estima...
arXiv Machine Learning
Aug 21
Gravitational-wave parameter estimation with machine-learning generated surrogate waveforms
arXiv:2608. 20222v1 Announce Type: cross Abstract: The worldwide network of gravitational-wave detectors have detected more than 350 binary coalescence events till date.
arXiv Machine Learning
Jul 7
Transformers with Physics-Informed Encodings and Simulation-Based Inference for Robust Detection of Eccentric Binary Black Holes in Pulsar Timing Array Data
arXiv:2607. 03904v1 Announce Type: new Abstract: Pulsar timing arrays (PTAs) provide a unique window into nanohertz gravitational waves (GWs), but extracting astrophysical parameters from noisy, long-baseline timing residuals remains computationally challenging with traditional Bayesian techniques due to the high dimensionality of the parameter space, complex and correlated noise models, and the cost of repeated likelihood evaluations.
arXiv Machine Learning
Jun 15
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.
arXiv Machine Learning
Jun 25
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
Sep 11
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.