A neural-network-based simulation inference method improves 3σ detection probability of gravitational-wave background anisotropies by 90-200% over Gaussian frequentist searches by learning non-Gaussian structure in pulsar timing residuals.
Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows,
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A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
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Detecting Gravitational-Wave Anisotropies with Simulation-Based Inference
A neural-network-based simulation inference method improves 3σ detection probability of gravitational-wave background anisotropies by 90-200% over Gaussian frequentist searches by learning non-Gaussian structure in pulsar timing residuals.
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Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.