Speaker
Description
Accurate and efficient predictions of scattering amplitudes are essential for precision studies in high-energy physics, particularly for multi-jet processes at collider experiments. In this work, we introduce a novel neural network architecture designed to predict amplitudes for multi-jet events. The model leverages the Catani–Seymour factorization scheme and uses MadGraph to compute amplitudes for related processes with fewer jets. By exploiting this factorization structure, the network learns to predict a correction factor that transforms the reduced-jet amplitude into the full multi-jet amplitude. This hybrid approach combines the strengths of theoretical factorization and data-driven learning, offering a promising direction for fast and scalable amplitude predictions.