Costs of Bayesian Parameter Estimation in Third-Generation Gravitational Wave Detectors: an Assessment of Current Acceleration Methods

TL;DR

This paper evaluates the computational costs of Bayesian parameter estimation for third-generation gravitational wave detectors, highlighting the need for more efficient methods due to the high data volume and computational demands.

Contribution

It systematically assesses the computational costs and compares acceleration techniques for Bayesian inference in 3G GW detectors, emphasizing their potential to reduce costs significantly.

Findings

Standard PE could require billions of CPU core hours for 3G data.

Acceleration methods can reduce this to less than a million core hours.

High-SNR scenarios require careful treatment of accelerated methods.

Abstract

Bayesian inference with stochastic sampling has been widely used to obtain the properties of gravitational wave (GW) sources. Although computationally intensive, its cost remains manageable for current second-generation GW detectors because of the relatively low event rate and signal-to-noise ratio (SNR). The third-generation (3G) GW detectors are expected to detect hundreds of thousands of compact binary coalescence (CBC) events every year with substantially higher SNR and longer signal duration, presenting significant computational challenges. In this study, we systematically evaluate the computational costs of CBC source parameter estimation (PE) in the 3G era by modeling the PE time cost as a function of SNR and signal duration. We examine the standard PE method alongside acceleration methods including relative binning, multibanding, and reduced order quadrature. We predict that PE for a one-month-observation catalog with 3G detectors could require at least billions of CPU core hours with the standard PE method, whereas acceleration techniques can reduce this demand to less than millions of core hours, which is as high as the cost of analyzing GW events in the past 10 years. These findings highlight the necessity for more efficient PE methods to enable cost-effective and environmentally sustainable data analysis for 3G detectors. In addition, we assess the accuracy of accelerated PE methods, emphasizing the need for careful treatment in high-SNR scenarios.