Normalizing Flows | A Quest After Perspectives

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

Tue, 29 Sep 2026 00:00:00 +0000

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

Wed, 15 Apr 2026 00:00:00 +0000

The framework studies inference under transient contamination and compares alternative encoders and training objectives. Its evaluation supplements coverage checks with a distributional scoring rule.

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arXiv abstract and submission history

Unlocking New Paths for Efficient Analysis of Gravitational Waves from Extreme-Mass-Ratio Inspirals with Machine Learning

Thu, 31 Jul 2025 00:00:00 +0000

The method targets the computational difficulty of EMRI posterior estimation. The published study notes that learned distributions can require refinement with conventional Bayesian sampling.

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Deep learning-driven likelihood-free parameter inference for 21-cm forest observations

Tue, 27 May 2025 00:00:00 +0000

Generative flows expand a limited simulation set, while inference flows estimate parameter posteriors from the forest power spectrum. The analysis uses simulated observations relevant to the Square Kilometre Array.