All Outputs (7)

Extremal black hole weather (2025)
Journal Article
Iuliano, C., Hollands, S., Green, S. R., & Zimmerman, P. (2025). Extremal black hole weather. Physical Review D, 111(12), Article 124038. https://doi.org/10.1103/PhysRevD.111.124038

We consider weakly nonlinear gravitational perturbations of a near-extremal Kerr black hole governed by the second-order vacuum Einstein equation. Using the GHZ [for S. R. Green et al., Classical Quantum Gravity 7, 075001 (2020)], these are parametri... Read More about Extremal black hole weather.

Real-time inference for binary neutron star mergers using machine learning (2025)
Journal Article
Dax, M., Green, S. R., Gair, J., Gupte, N., Pürrer, M., Raymond, V., Wildberger, J., Macke, J. H., Buonanno, A., & Schölkopf, B. (2025). Real-time inference for binary neutron star mergers using machine learning. Nature, 639(8053), 49-53. https://doi.org/10.1038/s41586-025-08593-z

Mergers of binary neutron stars emit signals in both the gravitational-wave (GW) and electromagnetic spectra. Famously, the 2017 multi-messenger observation of GW170817 (refs. 1,2) led to scientific discoveries across cosmology3, nuclear physics4,5,6... Read More about Real-time inference for binary neutron star mergers using machine learning.

Gravitational wave populations and cosmology with neural posterior estimation (2024)
Journal Article
Leyde, K., Green, S. R., Toubiana, A., & Gair, J. (2024). Gravitational wave populations and cosmology with neural posterior estimation. Physical Review D, 109(6), Article 064056. https://doi.org/10.1103/physrevd.109.064056

We apply neural posterior estimation for fast-and-accurate hierarchical Bayesian inference of gravitational wave populations. We use a normalizing flow to estimate directly the population hyper-parameters from a collection of individual source observ... Read More about Gravitational wave populations and cosmology with neural posterior estimation.

Relativistic Perturbation Theory for Black-Hole Boson Clouds (2024)
Journal Article
Cannizzaro, E., Sberna, L., Green, S. R., & Hollands, S. (2024). Relativistic Perturbation Theory for Black-Hole Boson Clouds. Physical Review Letters, 132(5), Article 051401. https://doi.org/10.1103/PhysRevLett.132.051401

We develop a relativistic perturbation theory for scalar clouds around rotating black holes. We first introduce a relativistic product and corresponding orthogonality relation between modes, extending a recent result for gravitational perturbations.... Read More about Relativistic Perturbation Theory for Black-Hole Boson Clouds.

Flow Matching for Scalable Simulation-Based Inference (2023)
Presentation / Conference Contribution
Wildberger, J., Dax, M., Green, S., Buchholz, S., Macke, J., & Schölkopf, B. (2023, December). Flow Matching for Scalable Simulation-Based Inference. Poster presented at Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, USA

Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional problems can be challenging. Building on recent advances in generative mo... Read More about Flow Matching for Scalable Simulation-Based Inference.

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference (2023)
Journal Article
Dax, M., Green, S. R., Gair, J., Pürrer, M., Wildberger, J., Macke, J. H., Buonanno, A., Macke, J. H., & Schölkopf, B. (2023). Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference. Physical Review Letters, 130(17), Article 171403. https://doi.org/10.1103/PhysRevLett.130.171403

We combine amortized neural posterior estimation with importance sampling for fast and accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian posterior using neural networks, and then attach importance weights base... Read More about Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference.

Real-Time Gravitational Wave Science with Neural Posterior Estimation (2021)
Journal Article
Dax, M., Green, S. R., Gair, J., Macke, J. H., Buonanno, A., & Schölkopf, B. (2021). Real-Time Gravitational Wave Science with Neural Posterior Estimation. Physical Review Letters, 127(24), Article 241103. https://doi.org/10.1103/physrevlett.127.241103

We demonstrate unprecedented accuracy for rapid gravitational wave parameter estimation with deep learning. Using neural networks as surrogates for Bayesian posterior distributions, we analyze eight gravitational wave events from the first LIGO-Virgo... Read More about Real-Time Gravitational Wave Science with Neural Posterior Estimation.