Neural Networks Enhance Gravitational Wave Ringdown Analysis Robustness

The research focuses on enhancing the robustness of gravitational wave (GW) ringdown analysis using amortized simulation-based inference. The study proposes an amortized neural posterior estimation strategy to estimate ringdown parameters directly. This method trains a neural density estimator of the posterior for all data segments within the prior range. The results demonstrate that the trained amortized network achieves statistically consistent parameter estimates with valid confidence coverage compared to established Markov-chain methods, while offering inference speeds that are orders of magnitude faster. The analysis also evaluates the robustness of the method against transient noise contamination, revealing that the timing of glitch injection has a decisive impact on estimation bias, particularly during the tail of a signal with sparse information. Glitch strength is positively correlated with estimation error, but has limited effect at low signal-to-noise ratios. Mass and spin parameters are found to be most sensitive to noise. This study not only provides an efficient and accurate inference framework for ringdown analysis but also lays a foundation for developing robust data-processing pipelines for future GW astronomy in realistic noise environments. The development of such robust data-processing pipelines is crucial for maximizing the scientific return from current and next-generation GW detectors, enabling more precise tests of general relativity in the strong field regime.

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