The paper presents a symbolic regression approach that derives an approximate expression for neutron‑star radii using only gravitational‑wave data from binary inspirals. By training on TOV solutions with piecewise polytropic equations of state, the authors obtain a formula that reproduces radii with differences of only a few hundred meters across a wide range of neutron‑star parameters. The method is validated on realistic dense‑matter EOSs and applied to the GW170817 event, comparing the inferred radii to existing distributions.
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.
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...
arXiv:2605.11280v2 Announce Type: replace-cross Abstract: Fast surrogate models for expensive simulations are now essential across the sciences, yet they typically operate as black boxes. We present...
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.
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.
arXiv:2609.09011v1 Announce Type: cross Abstract: The local dark matter density determines the strength of the signal expected in direct-detection experiments, yet published estimates from stellar mo...
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 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.
The paper introduces SINFONIA, a family of neural‑flow architectures designed to learn structure‑preserving evolution maps for long‑duration gravitational‑wave modelling. Three variants—symplectic, slimplectic, and Magnusian—are trained on a 2.5PN neutron‑star inspiral and demonstrate that long‑time accuracy is governed by a single secular channel linked to energy–angular‑momentum balance, allowing accurate integration over up to $10^{5}$ orbital periods with far fewer computational steps than traditional integrators. The learned maps also enable physics inference, recovering un‑modelled dissipative forces from accumulated phase information.
arXiv:2607. 29561v1 Announce Type: cross Abstract: Symbolic Regression (SR) aims to discover analytical equations from observational data and plays a central role in scientific modeling.
The paper introduces SINFONIA, a family of neural-flow architectures designed to learn finite-time evolution maps for orbital dynamics while preserving key physical structures. Three variants—symplectic/slimplectic, Taylor-anchored, and Magnusian—are applied to a 2.5PN neutron-star inspiral, demonstrating that long-term accuracy depends on a single secular channel tied to energy–angular-momentum balance rather than pointwise errors. These learned maps achieve accurate phase evolution over up to $10^{5}$ orbital periods at lower computational cost than traditional integrators, and can also be used to infer unmodeled forces from accumulated phase data.