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.
Rapid Parameter Estimation for Pulsar-Timing-Array Datasets with Variational Inference and Normalizing Flows,
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A frequentist CGW search method with non-parametric red noise suppression and optimal pulsar selection achieves Bayesian-comparable accuracy on NANOGrav-based simulations at far lower computational cost.
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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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Scalable continuous gravitational wave detection in PTA data with non-parametric red noise suppression and optimal pulsar selection
A frequentist CGW search method with non-parametric red noise suppression and optimal pulsar selection achieves Bayesian-comparable accuracy on NANOGrav-based simulations at far lower computational cost.