Architecture Acceleration of Machine Learning Gravitational Waveform Surrogate Models

Abstract

Accurate and computationally efficient waveform models of compact binary coalescences are a fundamental requirement for gravitational wave data analysis. This work presents differentiable and hardware-accelerated implementations of the machine-learning surrogate models mlgw and mlgw-bns within the JAX ecosystem. The proposed framework enables JIT compilation, vector paralellization, native GPU acceleration, and automatic differentiation, while preserving the original surrogate training infrastructure and its applicability to arbitrary non-precessing waveform approximants. Benchmarks are performed in the binary black hole case with a newly trained surrogate of the SEOBNRv5HM approximant; on CPU, they show speed-ups in waveform evaluation time with respect to the original mlgw implementation that exceed one order of magnitude; further, when exploiting GPU acceleration and large-batch vectorization these reach approximately two orders of magnitude. The benchmarks performed in the case of binary neutron stars with a TEOBResumSPA surrogate model achieve similar gains on GPU. Our JAX-based surrogate modeling framework can be integrated into Bayesian parameter estimation pipelines through GWgpu-jax, an open-source package that connects JAX-compatible gravitational waveform generators to JAX-native sampling algorithms. We demonstrate this capability with a nested-sampling pipeline built upon BlackJax-NS, performing parameter estimation analyses of the GW150914 and GW170817 events with mlgw and mlgw-bns. The analyses require approximately twelve and seventeen minutes, respectively, on a single GPU and yield posterior distributions consistent with those reported by the LIGO-Virgo-KAGRA Collaboration. Beyond nested sampling, JAX automatic differentiation provides efficient gradient and Hessian evaluations of the waveform models, enabling integration with gradient-based Bayesian inference methods.