5 papers
Monte Carlo Event Generation with Continuous Normalizing Flows
Enrico Bothmann, Timo JanÃen, Max Knobbe +2
We apply Continuous Normalizing Flows trained with the Flow Matching method to the problem of phase-space sampling in Monte Carlo event generation for high-energy collider physics.…
Accelerating multijet-merged event generation with neural network matrix element surrogates
Tim Herrmann, Timo JanÃen, Mathis Schenker +2
The efficient simulation of multijet final states presents a serious computational task for analyses of LHC data and will be even more so at the HL-LHC. We here discuss means to ac…
Sampling NNLO QCD phase space with normalizing flows
Timo JanÃen, Rene Poncelet, Steffen Schumann
We showcase the application of neural importance sampling for the evaluation of NNLO QCD scattering cross sections. We consider Normalizing Flows in the form of discrete Coupling L…
Efficient many-jet event generation with Flow Matching
Enrico Bothmann, Timo JanÃen, Max Knobbe +2
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…
Phase space sampling with Markov Chain Monte Carlo methods
Salvatore La Cagnina, Cornelius Grunwald, Timo JanÃen +2
We present a study on using Markov Chain Monte Carlo (MCMC) techniques to explore the high-dimensional and multi-modal phase space of scattering events at high-energy particle coll…