Speaker
Description
Fast waveform surrogates are essential for gravitational-wave inference, but neural surrogates typically lack uncertainty estimates and offer little guidance on how accuracy scales with training resources. We present an autoregressive flow-matching surrogate for binary-black-hole waveforms that operates on amplitude-phase representations and samples each successive segment with a conditional flow. A compact, few-million-parameter model attains surrogate-grade mismatches, with phase coherence maintained over the full inspiral-merger-ringdown, and stochastic sampling provides uncertainty estimates at no additional training cost. We further measure an empirical scaling law relating accuracy to training-set size, which successfully predicted the performance of our largest training run in advance, offering a quantitative recipe for surrogate development budgets. Across all scales, the dominant residual error is a global phase/time reference offset rather than accumulated instability, identifying anchoring as the central challenge for autoregressive waveform generation. We discuss implications for waveform foundation models and extensions to higher modes and eccentric systems.