Sampler-free gravitational wave inference using matrix multiplication

TL;DR

This paper introduces a fast, sampler-free method for gravitational wave parameter estimation that leverages matrix multiplication, enabling rapid analysis of large waveform banks with minimal computational resources.

Contribution

The authors develop a novel matrix multiplication-based approach for gravitational wave inference that eliminates the need for stochastic sampling, significantly reducing computation time.

Findings

Full parameter estimation in minutes on a single CPU

Supports large waveform banks of up to 10^6 waveforms

Enables sensitive searches with full evidence calculation

Abstract

Parameter estimation (PE) for compact binary coalescence (CBC) events observed by gravitational wave (GW) laser interferometers is a core task in GW astrophysics. We present a method to compute the posterior distribution efficiently without relying on stochastic samplers. First, we show how to select sets of intrinsic and extrinsic parameters that efficiently cover the relevant phase space. We then show how to compute the likelihood for all combinations of these parameters using dot products. We describe how to assess and tune the integration accuracy, making the outcome predictable and adaptable to different applications. The low computational cost allows full PE in minutes on a single CPU, with the potential for further acceleration using multiple CPUs or GPUs. We implement this method in the $\texttt{dot-PE}$ package, enabling sensitive searches using the full evidence integral for precessing CBCs and supporting large waveform banks ($\sim10^5$--$10^6$ waveforms), regardless of waveform generation cost.