Binary Black Hole Parameter Estimation with Hybrid CNN-Transformer Neural Networks

Abstract:The detection of gravitational waves has revolutionized our ability to explore fundamental aspects of the Universe. Traditionally, modeled gravitational-wave signals have been identified using template-based matched filtering, followed by coincidence analysis across multiple detectors in the signal-to-noise ratio time series. Recent advances in Machine Learning and Deep Learning have sparked growing interest in their application to both signal detection and parameter estimation. In this study, a hybrid Deep Learning strategy is proposed that leverages the effectiveness of Transformer encoders alongside well-established Convolutional Neural Network architectures in an attempt to estimate the intrinsic and extrinsic parameters of non-precessing binary black hole systems. The primary focus of this work is point estimation, producing single best-fit values for each parameter rather than full posterior distributions. This method is evaluated on both simulated signals embedded in Gaussian noise and real gravitational-wave events, and it demonstrates strong predictive performance and robustness across key astrophysical parameters.

Accepted manuscript. 12 pages, 10 figures

General Relativity and Quantum Cosmology (gr-qc); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)

arXiv:2606.13941 [gr-qc]

(or arXiv:2606.13941v1 [gr-qc] for this version)

https://doi.org/10.48550/arXiv.2606.13941 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Astronomy and Computing, vol. 54, 101027 (2026)

https://doi.org/10.1016/j.ascom.2025.101027 Focus to learn more DOI(s) linking to related resources

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