Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows

TL;DR

This paper introduces a deep learning framework combining normalizing flows, contrastive learning, and a neural glitch generator to improve parameter inference accuracy and robustness for space-based gravitational-wave data contaminated by glitches.

Contribution

The authors develop a novel glitch-robust amortized inference method that outperforms traditional MCMC, incorporating a neural glitch generator and advanced evaluation metrics.

Findings

The proposed method yields more accurate posteriors under glitch contamination.

The full time-frequency model with contrastive learning performs best overall.

Standard coverage diagnostics are insufficient; the continuous ranked probability score offers a stricter assessment.

Abstract

Transient noise artifacts, commonly referred to as glitches, pose a major challenge to parameter inference for space-based gravitational-wave (GW) observations. We develop a glitch-robust amortized inference framework for massive black hole binaries in the Taiji detector configuration by combining conditional normalizing flows, a time-frequency multimodal fusion encoder, and contrastive learning. To enable large-scale training on contaminated data, we further introduce a neural glitch generator that produces high-fidelity synthetic transients at substantially reduced computational cost. Systematic experiments show that, under glitch contamination, the proposed method yields more accurate and better-calibrated posteriors than a conventional Markov Chain Monte Carlo baseline. In ablation studies, the full time-frequency model with contrastive learning performs best overall and remains robust to variations in glitch duration and merger-relative timing. We further show that standard coverage diagnostics alone are insufficient to fully assess posterior fidelity. We therefore complement them with the continuous ranked probability score, which provides a stricter assessment of global distributional agreement in non-ideal GW data. Taken together, these results establish deep-learning-based amortized inference as a promising framework for fast and robust Bayesian parameter estimation in future space-based GW observations.