7 citations · 15 across the 6 of their papers we have counts for
6 papers
Too good to go: Upcycling Phase-Space Points for Multijet Processes
Konrad Helms, Timo Janßen, Steffen Schumann
The efficient sampling of high-dimensional phase spaces is a major challenge for Monte Carlo event generators, as for high-multiplicity final states the evaluation of scattering ma…
Monte Carlo Event Generation with Continuous Normalizing Flows
Enrico Bothmann, Timo Janßen, 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.…
Efficient many-jet event generation with Flow Matching
Enrico Bothmann, Timo Janßen, 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…
Accelerating multijet-merged event generation with neural network matrix element surrogates
Tim Herrmann, Timo Janßen, 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 Janßen, 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…
Phase space sampling with Markov Chain Monte Carlo methods
Salvatore La Cagnina, Cornelius Grunwald, Timo Janßen +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…