Architecture Acceleration of Machine Learning Gravitational Waveform Surrogate Models

Accurate and computationally efficient waveform models of compact binary coalescences are a fundamental requirement for gravitational wave data analysis. The new analysis still awaits peer review, but it already lays out the central claim clearly.

That matters because astrophysics becomes persuasive only when an observed signal can be tied to a physically defensible explanation. Compact objects such as neutron stars and black holes are natural laboratories for extreme physics, but the distance and complexity of these systems make interpretation difficult without multi-wavelength coverage and careful modeling. A detection without a mechanism is only half a result. the other half comes from showing that the signal fits quantitatively inside a coherent physical picture rather than merely being consistent with a broad family of models. 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.

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.

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. The analyses require approximately twelve and seventeen minutes, respectively, on a single GPU and yield posterior distributions consistent with those reported by the.

The broader interest lies in turning an observational clue into something that can be weighed against competing models of the underlying physics. Astrophysics does not have the luxury of controlled experiments; everything is inferred from radiation that traveled across cosmic distances under conditions that cannot be reproduced in a terrestrial laboratory. This makes the interpretation chain longer and more uncertain than in bench science, but it also means that a well-constrained measurement of an extreme object carries theoretical information that no earthbound experiment can provide.

Beyond nested sampling, JAX automatic differentiation provides efficient gradient and Hessian evaluations of the waveform models, enabling integration with gradient-based Bayesian. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy.

Because this is still a preprint, the result should be read with genuine interest and proportionate caution. Peer review is not a guarantee of correctness, but it is a process that forces authors to respond to technical criticism from specialists who have no stake in a particular outcome. Preprints that survive that process, often with substantive revisions, emerge with a stronger evidential base than the version that first appeared. Until that stage is complete, the responsible reading keeps uncertainty explicitly visible rather than treating the claims as established findings.

The next step is to see whether independent datasets and physical modeling converge on the same interpretation. Multi-wavelength follow-up, combining X-ray, radio and optical data where possible, is typically what separates a compelling detection from a robust physical characterization. In high-energy astrophysics, results that initially looked definitive have been revised when data from a second messenger arrived; the current result should be read with that history in mind. Until peer review and independent follow-up address those open questions, skepticism is not a failure of appreciation for the work; it is part of how science decides what to keep.

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