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...
By Rohan Raha, Prayush Kumar
arXiv:2608. 20222v1 Announce Type: cross Abstract: The worldwide network of gravitational-wave detectors have detected more than 350 binary coalescence events till date.
By Suyog Garg, Kipp Cannon
arXiv:2606. 13941v1 Announce Type: cross Abstract: The detection of gravitational waves has revolutionized our ability to explore fundamental aspects of the Universe.
By Panagiotis N. Sakellariou, Spiros V. Georgakopoulos, Sotiris Tasoulis, Vassilis P. Plagianakos
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.
By Muhammad Adnan Shahzad
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.
By Ann-Kristin Malz, Gregory Ashton, Nicolo Colombo
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.