The Sequential Monte Carlo goes NUTS: Boosting Gravitational-Wave Inference

Abstract

Sequential Monte Carlo (SMC) methods have recently been applied to gravitational-wave inference as a powerful alternative to standard sampling techniques, such as Nested Sampling. At the same time, gradient-based Markov Chain Monte Carlo algorithms, most notably the No-U-Turn Sampler (NUTS), provide an efficient way to explore high-dimensional parameter spaces. In this work we present SHARPy, a Bayesian inference framework that combines the parallelism and evidence-estimation capabilities of SMC with the state-of-the-art sampling performance of NUTS. Moreover, SHARPy exploits the local geometric structure of the posterior to further improve efficiency. Built on JAX, a high-performance computing framework that enables automatic differentiation and hardware acceleration, SHARPy performs gravitational-wave inference on binary black-hole events in around ten minutes, yielding posterior samples and Bayesian evidence estimates that are consistent with those obtained through Nested Sampling. This work sets a new milestone in Gravitational-Wave inference with likelihood-based methods and paves the way for model comparison tasks to be accomplished in minutes.

Matches the published version

References in corpus (36)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Array Programming with NumPy
• Observation of Gravitational Waves from a Binary Black Hole Merger
• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Advanced LIGO
• MCMC using Hamiltonian dynamics
• dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences
• The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
• Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy
• Frequency-domain gravitational waves from non-precessing black-hole binaries. II. A phenomenological model for the advanced detector era
• Interferometer design of the KAGRA gravitational wave detector
• Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
• Frequency-domain gravitational waves from non-precessing black-hole binaries. I. New numerical waveforms and anatomy of the signal
• A simple model of complete precessing black-hole-binary gravitational waveforms
• Bayesian inference for compact binary coalescences with BILBY: Validation and application to the first LIGO--Virgo gravitational-wave transient catalogue
• An introduction to Bayesian inference in gravitational-wave astronomy: parameter estimation, model selection, and hierarchical models
• A guide to LIGO-Virgo detector noise and extraction of transient gravitational-wave signals
• Real-time gravitational-wave science with neural posterior estimation
• Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
• Fast and Accurate Inference on Gravitational Waves from Precessing Compact Binaries
• Nested sampling for physical scientists
• Nested Sampling with Normalising Flows for Gravitational-Wave Inference
• Learning Bayesian posteriors with neural networks for gravitational-wave inference
• PESummary: the code agnostic Parameter Estimation Summary page builder
• Parameter estimation with gravitational waves
• Accelerating parameter estimation of gravitational waves from compact binary coalescence using adaptive frequency resolutions
• Accelerating astronomical and cosmological inference with Preconditioned Monte Carlo
• Rapid Parameter Estimation of Gravitational Waves from Binary Neutron Star Coalescence using Focused Reduced Order Quadrature
• Removing degeneracy and multimodality in gravitational wave source parameters
• Sampling the full hierarchical population posterior distribution in gravitational-wave astronomy
• A Hamiltonian Monte Carlo method for Bayesian Inference of Supermassive Black Hole Binaries
• GWTC-4.0: Population Properties of Merging Compact Binaries
• BlackJAX: Composable Bayesian inference in JAX
• Deep learning to detect gravitational waves from binary close encounters: Fast parameter estimation using normalizing flows
• Validating Sequential Monte Carlo for Gravitational-Wave Inference
• Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo