Mathias Backes, Anja Butter, Tilman Plehn, Ramon Winterhalder
SciPost Phys. 10, 089 (2021) ·
published 23 April 2021
Event generation with neural networks has seen significant progress recently.
The big open question is still how such new methods will accelerate LHC
simulations to the level required by upcoming LHC runs. We target a known
bottleneck of standard simulations and show how their unweighting procedure
can be improved by generative networks. This can, potentially, lead to a very
significant gain in simulation speed.
SciPost Phys. 10, 084 (2021) ·
published 22 April 2021
Semi-visible jets arise in strongly interacting dark sectors, where parton
evolution includes dark sector emissions, resulting in jets overlapping with
missing transverse momentum. The implementation of semi-visible jets is done
using the Pythia Hidden valley module to duplicate the QCD sector showering. In
this work, several jet substructure observables have been examined to compare
semi-visible jets and light quark/gluon jets. These comparisons were performed
using different dark hadron fraction in the semi-visible jets (signal). The
extreme scenarios where signal consists either of entirely dark hadrons or
visible hadrons offers a chance to understand the effect of the specific dark
shower model employed in these comparisons. We attempt to decouple the
behaviour of jet-substructure observables due to inherent semi-visible jet
properties, from model dependence owing to the existence of only one dark
shower model as mentioned above.