In a controlled binary-black-hole benchmark, soft learned artifact-aware interval rescaling (LAIR) reduces marginal calibration error for frequency masks from 0.1195 to 0.0672 but is not uniformly better than raw intervals and is positioned as a diagnostic rather than a replacement for full posterio
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Artifact-Conditioned Interval Diagnostics for Flow-Matching Neural Posterior Estimation in a Controlled Gravitational-Wave Benchmark
In a controlled binary-black-hole benchmark, soft learned artifact-aware interval rescaling (LAIR) reduces marginal calibration error for frequency masks from 0.1195 to 0.0672 but is not uniformly better than raw intervals and is positioned as a diagnostic rather than a replacement for full posterio