Fast simulation of the CMS detector at the LHC
By A. Schmidt. 2008
226 citations recorded by the source corpus.
Subfields: Nuclear and High Energy Physics, AI for Physical Sciences
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Nuclear and High Energy Physics
- Cms Collaboration
- Benjamin Nachman
- M. Raidal
- Jean-Roch Vlimant
- Atlas Collaboration
- É. Conte
- S. Visscher
- Luke de Oliveira
In AI for Physical Sciences
- Benjamin Nachman
- Luke de Oliveira
- S. Visscher
- Michela Paganini
- Salvatore Rappoccio
- M. Selvaggi
- D. Gulhan
- Huilin Qu
Citations in the corpus
- Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis (2017, background): “Various approximations can be used to make faster simulations [15–17], but the time is still non-negligible and they are not applicable in all physics applications.”
- Search for new physics in the multijet and missing transverse momentum final state in proton-proton collisions at $ \sqrt{s} $ = 8 TeV (2014, methodology): “The SUSY signal samples are generated using MADGRAPH5, the CTEQ6L [37] parton distribution functions (PDF), and are simulated using the CMS fast simulation package [38].”
- Combined search for electroweak production of charginos and neutralinos in proton-proton collisions at $\sqrt{s} =$ 13 TeV (2018, methodology): “The response of the CMS detector for SM background samples is simulated using a Geant4based model [85], while that for new physics signals is performed using the CMS fast simulation package [86].”
- Search for electroweak production of charginos and neutralinos in multilepton final states in proton-proton collisions at s=13$$ \sqrt{s}=13 $$ TeV (2017, methodology): “SUSY particles, are also modelled with PYTHIA, while the detector simulation is performed with the CMS fast simulation package [64].”