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Learning Instrumental Noise Profiles in Signal-Confusion Scenarios for Space-Based Gravitational-Wave Detectors

Dingkai Zhang, He Wang2

  • 2 Corresponding author

JournalResearch in Astronomy and Astrophysics (2026)

First public: Published online:

  • Detection & denoising
  • Parameter inference

Research summary

Overlapping Galactic binaries complicate instrumental-noise estimation for space-based gravitational-wave detectors. This study combines population-level generative modeling, convolutional foreground prediction, and spectral-covariance inference for simulated Taiji observations. It treats residual foreground mismatch explicitly, allowing uncertainty in foreground subtraction to propagate into estimates of the instrumental-noise profile.

Extended notes

The analysis links simulated foreground subtraction to Bayesian noise inference rather than assuming a signal-free interval. Smooth residual-model corrections account for foreground mismatch in the spectral likelihood.

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