Enhancing Taiji's Parameter Estimation under Non-Stationarity: a Time-Frequency Domain Framework for Galactic Binaries and Instrumental Noises

TL;DR

This paper introduces a time-frequency domain framework using short-time Fourier transform to improve parameter estimation of Galactic binaries and instrumental noise in space-based gravitational wave data affected by non-stationary noise.

Contribution

It presents a novel STFT-based Bayesian inference method that outperforms traditional frequency-domain analysis in non-stationary noise conditions for gravitational wave data.

Findings

Reduces estimation uncertainty and bias.

Recovers low SNR signals missed by frequency methods.

Mitigates noise parameter degeneracy.

Abstract

The data analysis of space-based gravitational wave detectors like Taiji faces significant challenges from non-stationary noise, which compromises the efficacy of traditional frequency-domain analysis. This work proposes a unified framework based on short-time Fourier transform (STFT) to enhance parameter estimation of Galactic binary and characterization of instrumental noise under non-stationarity. Segmenting data into locally stationary intervals, we derive STFT-based models for signals and noises, and implement Bayesian inference via the extended Whittle likelihood. Validated through the analysis of verification Galactic binaries and instrumental noises, our STFT approach outperforms frequency-domain methods by reducing the uncertainty and bias of estimation, successfully recovering low signal-to-noise ratio signals missed by frequency-domain analysis, and mitigating the degeneracy among noise parameters. The framework's robustness against noise drifts and computational efficiency highlight its potential for integration into future global analysis pipelines.