Göttingen 2025 – wissenschaftliches Programm
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T: Fachverband Teilchenphysik
T 76: Data, AI, Computing, Electronics VII (Generative AI, MC Generators)
T 76.8: Vortrag
Donnerstag, 3. April 2025, 18:00–18:15, VG 2.101
Navigating Phase Space for Event Generation: interfacing Sherpa with BAT.jl — Cornelius Grunwald1, Timo Janssen2, Kevin Kröninger1, •Salvatore La Cagnina1, and Steffen Schumann2 — 1TU Dortmund University, Dortmund, Germany — 2Georg-August-Universität Göttingen, Germany
The generation of Monte Carlo events is a crucial step for all particle collider experiments. A major challenge in event generation is the efficient sampling of the phase spaces of hard scattering processes due to the potentially large number and complexity of Feynman diagrams and their interference and divergence structures. In this presentation, we address the challenges of efficient Monte Carlo event generation and demonstrate improvements that can be achieved through the application of advanced sampling techniques. We highlight that using the algorithms implemented in BAT.jl for sampling the phase spaces given by Sherpa offers great flexibility in the choice of sampling algorithms and has the potential to significantly enhance the efficiency of event generation. By interfacing BAT.jl, a package designed for Bayesian analyses that offers a collection of modern sampling algorithms, with the Sherpa event generator, we aim to improve the efficiency of phase space exploration and Monte Carlo event generation. We combine the physics-informed multi-channel sampling approach of Sherpa with advanced sampling techniques such as Markov Chain Monte Carlo (MCMC) and Nested Sampling.
Keywords: Sampling; Sherpa; Event Generation; Markov Chain Monte Carlo; MC event generation