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
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.
By Bhavya Gupta, Deep Chatterjee, William Benoit, Ethan Marx, Christina Reissel, Seiya Tsukamoto, Kyungseop Yoon, Michael W. Coughlin, Philip Harris, Erik Katsavounidis
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: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.
By Annalena Kofler, Maximilian Dax, Stephen R. Green, Jonas Wildberger, Nihar Gupte, Jakob H. Macke, Jonathan Gair, Alessandra Buonanno, Bernhard Sch\"olkopf
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
This paper presents a reproducibility audit of frozen‑encoder anomaly detection experiments originally reported on arXiv. The authors confirm that the numerical discrimination results can be reproduced from the preserved artifacts, but they find that the claimed causal link to interferometric pretraining is unsupported. They show that near‑zero embeddings and architectural choices, rather than a morphological prior from gravitational‑wave instrumentation, explain the observed anomaly‑detection performance.
By Jose S\'anchez Andreu
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: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. 29339v1 Announce Type: cross Abstract: Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring.
By Isao Kurosawa
The study presents a deep‑learning approach (YOLOv11) trained on SDSS images to distinguish genuine dual active galactic nuclei (DAGN) from chance superpositions and foreground stars in the GOTHIC survey. Applying the model to 46,061 previously rejected candidates yields 29,605 dual‑nucleus candidates, with 54.5–62 % likely genuine, and a conservative subset of ~13,672 compact systems. The resulting catalogue refines the DAGN candidate list, reducing contamination and expanding the plausible census, though spectroscopic confirmation remains needed.
By Bhavesh Mukheja, Snehanshu Saha, Anwesh Bhattacharya, Mousumi Das, Fran\c{c}oise Combes, Sudhanshu Barway
Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring. In each setting a detector must stay reliable both when sensors fail and when the signal is buried in noise.
arXiv:2604.13687v2 Announce Type: replace-cross
Abstract: Glitches frequently contaminate data in gravitational-wave detectors, complicating the observation and analysis of astrophysical signals. Thi...
By Tiago Fernandes, Francesco Di Renzo, Antonio Onofre, Alejandro Torres-Forn\'e, Jos\'e A. Font