An architecture for efficient gravitational wave parameter estimation with multimodal linear surrogate models

TL;DR

This paper introduces a fast, analytic likelihood evaluation method using surrogate models for gravitational wave parameter estimation, significantly reducing computational costs and enabling rapid analysis of complex waveforms.

Contribution

It presents a novel approach that combines surrogate models with a new parameter estimation pipeline for efficient gravitational wave analysis.

Findings

First use of these models for parameter estimation

Enabled rapid analysis of multi-modal numerical relativity waveforms

Demonstrated effectiveness with effective-one-body and numerical relativity waveforms

Abstract

The recent direct observation of gravitational waves has further emphasized the desire for fast, low-cost, and accurate methods to infer the parameters of gravitational wave sources. Due to expense in waveform generation and data handling, the cost of evaluating the likelihood function limits the computational performance of these calculations. Building on recently developed surrogate models and a novel parameter estimation pipeline, we show how to quickly generate the likelihood function as an analytic, closed-form expression. Using a straightforward variant of a production-scale parameter estimation code, we demonstrate our method using surrogate models of effective-one-body and numerical relativity waveforms. Our study is the first time these models have been used for parameter estimation and one of the first ever parameter estimation calculations with multi-modal numerical relativity waveforms, which include all l <= 4 modes. Our grid-free method enables rapid parameter estimation for any waveform with a suitable reduced-order model. The methods described in this paper may also find use in other data analysis studies, such as vetting coincident events or the computation of the coalescing-compact-binary detection statistic.