Speaker
Description
We apply for the first time the Flow Matching method to the problem of phase-space sampling for event generation in high-energy collider physics. By training the model to remap the random numbers used to generate the momenta and helicities of the collision matrix elements as implemented in the portable partonic event generator Pepper, we find substantial efficiency improvements in the studied processes. We focus our study on the computationally most relevant highest final-state multiplicities in Drell-Yan and top-antitop pair production used in simulated samples for the Large Hadron Collider, and find that the unweighting efficiencies improve by factors of 80 and 10, respectively, when compared to the standard approach of using a Vegas-based optimisation. For lower multiplicities we find factors up to 100. We also compare Continuous Normalizing Flows trained with Flow Matching against the previously studied Normalizing Flows based on Coupling Layers and find that the former leads to better results, faster training and a better scaling behaviour across the studied multiplicity range.
