Advancing Space-Based Gravitational Wave Astronomy: Rapid Parameter Estimation via Normalizing Flows

TL;DR

This paper introduces a normalizing flow-based method for rapid and accurate parameter estimation of massive black hole binaries in space-based gravitational wave data, overcoming complex noise and response challenges.

Contribution

It presents a scalable normalizing flow approach that simplifies data complexity and enables fast, precise inference in the Taiji space-based GW environment, including multimodal parameters.

Findings

Estimates MBHB parameters several orders faster than traditional methods.

Maintains high accuracy even in complex, noisy backgrounds.

Unveils multimodality in the arrival time parameter.

Abstract

Gravitational wave (GW) astronomy is witnessing a transformative shift from terrestrial to space-based detection, with missions like Taiji at the forefront. While the transition brings unprecedented opportunities for exploring massive black hole binaries (MBHBs), it also imposes complex challenges in data analysis, particularly in parameter estimation amidst confusion noise. Addressing this gap, we utilize scalable normalizing flow models to achieve rapid and accurate inference within the Taiji environment. Innovatively, our approach simplifies the data's complexity, employs a transformation mapping to overcome the year-period time-dependent response function, and unveils additional multimodality in the arrival time parameter. Our method estimates MBHBs several orders of magnitude faster than conventional techniques, maintaining high accuracy even in complex backgrounds. These findings significantly enhance the efficiency of GW data analysis, paving the way for rapid detection and alerting systems and enriching our ability to explore the universe through space-based GW observation.