Accelerated Sequential Posterior Inference via Reuse for Gravitational-Wave Analyses

TL;DR

ASPIRE is a new framework that efficiently updates gravitational-wave analysis results under different models without rerunning full analyses, saving significant computational resources.

Contribution

It introduces a novel combination of normalizing flows and Sequential Monte Carlo to enable unbiased reanalysis of existing posterior samples under new models.

Findings

Reduces reanalysis computational cost by 4-10 times

Accurately reproduces Bayesian results for different waveform models

Facilitates systematic studies and scalable reanalyses

Abstract

We introduce Accelerated Sequential Posterior Inference via Reuse (ASPIRE), a broadly applicable framework that transforms existing posterior samples and Bayesian evidence estimates into unbiased results under alternative models without rerunning the original analysis. ASPIRE combines normalizing flows with a generalized Sequential Monte Carlo scheme, enabling efficient updates of existing results and reducing the computational cost of reanalyses by 4-10 times. This addresses a growing problem in gravitational-wave astronomy, where events must be repeatedly reanalyzed under different models or physical hypotheses. We show that ASPIRE reproduces full Bayesian results when switching waveform models or adding physical effects such as spin precession and orbital eccentricity. With this statistical robustness, ASPIRE turns repeated reanalyses into fast, reliable updatespaving the way for systematic studies of waveform systematics, scalable reanalyses across large event catalogs, and broadly applicable Bayesian reanalysis across other scientific domains.