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.
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:2606. 13941v1 Announce Type: cross Abstract: The detection of gravitational waves has revolutionized our ability to explore fundamental aspects of the Universe.
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:2607. 01372v1 Announce Type: cross Abstract: Gravitational Waves (GWs) represent the newest window of astronomy, furthering our understanding of compact objects like black holes and neutron stars in the Universe.
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.
The paper proposes a weighted conformal prediction framework to combine outputs from multiple gravitational‑wave detection 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 for events near the detection threshold, enabling recovery of true signals that would otherwise be missed.
arXiv:2609.39583v1 Announce Type: cross Abstract: We present the results of \textbf{MADGRAV}, a deep-learning-based search for high-mass compact binary coalescences, applied to the data collected by...
arXiv:2609.14277v1 Announce Type: cross Abstract: Ringdown gravitational waves from binary black hole mergers can be modeled as superpositions of quasinormal modes (QNMs), whose frequencies and excit...
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:2604. 09787v2 Announce Type: replace-cross Abstract: Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a signal from measurement-dependent artifacts from the sensor or instrument.
arXiv:2605.11269v2 Announce Type: replace-cross Abstract: Modern gravitational wave astronomy relies on modeling tasks that often require months of graduate-level effort, including building fast wave...