Contributor info: Dr Frank Siegert

Katharina Danziger, Timo Janßen, Steffen Schumann, Frank Siegert

SciPost Phys. 12, 164 (2022) · published 18 May 2022 | Toggle abstract · pdf

The generation of unit-weight events for complex scattering processes presents a severe challenge to modern Monte Carlo event generators. Even when using sophisticated phase-space sampling techniques adapted to the underlying transition matrix elements, the efficiency for generating unit-weight events from weighted samples can become a limiting factor in practical applications. Here we present a novel two-staged unweighting procedure that makes use of a neural-network surrogate for the full event weight. The algorithm can significantly accelerate the unweighting process, while it still guarantees unbiased sampling from the correct target distribution. We apply, validate and benchmark the new approach in high-multiplicity LHC production processes, including $Z/W$+4 jets and $t\bar{t}$+3 jets, where we find speed-up factors up to ten.

Christian Bierlich, Andy Buckley, Jonathan Butterworth, Christian Holm Christensen, Louie Corpe, David Grellscheid, Jan Fiete Grosse-Oetringhaus, Christian Gutschow, Przemyslaw Karczmarczyk, Jochen Klein, Leif Lonnblad, Christopher Samuel Pollard, Peter Richardson, Holger Schulz, Frank Siegert

SciPost Phys. 8, 026 (2020) · published 11 February 2020 | Toggle abstract · pdf

First released in 2010, the Rivet library forms an important repository for analysis code, facilitating comparisons between measurements of the final state in particle collisions and theoretical calculations of those final states. We give an overview of Rivet's current design and implementation, its uptake for analysis preservation and physics results, and summarise recent developments including propagation of MC systematic-uncertainty weights, heavy-ion and $ep$ physics, and systems for detector emulation. In addition, we provide a short user guide that supplements and updates the Rivet user manual.

Enrico Bothmann, Gurpreet Singh Chahal, Stefan Höche, Johannes Krause, Frank Krauss, Silvan Kuttimalai, Sebastian Liebschner, Davide Napoletano, Marek Schönherr, Holger Schulz, Steffen Schumann, Frank Siegert

SciPost Phys. 7, 034 (2019) · published 18 September 2019 | Toggle abstract · pdf

Sherpa is a general-purpose Monte Carlo event generator for the simulation of particle collisions in high-energy collider experiments. We summarize essential features and improvements of the Sherpa 2.2 release series, which is heavily used for event generation in the analysis and interpretation of LHC Run 1 and Run 2 data. We highlight a decade of developments towards ever higher precision in the simulation of particle-collision events.