Accurate and efficient amplitude predictions are essential for precision studies of multi-jet processes at the LHC. We introduce a novel neural network architecture that predicts multi-jet amplitudes by leveraging the Catani–Seymour factorization scheme and related lower-jet amplitudes, requiring the network to learn only a correction factor. This hybrid approach combines theoretical factorization with a data-driven Ansatz, enabling fast and scalable amplitude predictions. Our networks also estimate the accuracy of each prediction, allowing us to selectively use results that meet a predefined accuracy threshold. In the context of leading-order event generation, this approach achieves speed-up factors of up to 20 while maintaining percent-level accuracy for all observables.
Beccatini et al. (Tue,) studied this question.