All Outputs (3)

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.