High-multiplicity events remain a bottleneck for LHC simulations due to their computational cost. We present a ML-surrogate approach to accelerate matrix element reweighting from leading-color (LC) to full-color (FC) accuracy, building on recent advancements in LC event generation. Comparing a variety of modern network architectures for representative QCD processes, we achieve speed-up of around a factor two over the current LC-to-FC baseline. We also show how transformers learn and exploit underlying symmetries, to improve generalization. Given the gained trust in trained networks and developments in learned uncertainties, the LC-to-FC approach will eventually benefit further from not needing a final classic unweighting step.
@article{arxiv.2509.07068,
title = {FASTColor -- Full-color Amplitude Surrogate Toolkit for QCD},
author = {Javier Mariño Villadamigo and Rikkert Frederix and Tilman Plehn and Timea Vitos and Ramon Winterhalder},
journal= {arXiv preprint arXiv:2509.07068},
year = {2025}
}