SCYNet: Testing supersymmetric models at the LHC with neural networks
arXiv:1703.01309 · doi:10.1140/epjc/s10052-017-5224-8
Abstract
SCYNet (SUSY Calculating Yield Net) is a tool for testing supersymmetric models against LHC data. It uses neural network regression for a fast evaluation of the profile likelihood ratio. Two neural network approaches have been developed: one network has been trained using the parameters of the 11-dimensional phenomenological Minimal Supersymmetric Standard Model (pMSSM-11) as an input and evaluates the corresponding profile likelihood ratio within milliseconds. It can thus be used in global pMSSM-11 fits without time penalty. In the second approach, the neural network has been trained using model-independent signature-related objects, such as energies and particle multiplicities, which were estimated from the parameters of a given new physics model. While the calculation of the energies and particle multiplicities takes up computation time, the corresponding neural network is more general and can be used to predict the LHC profile likelihood ratio for a wider class of new physics models.
19 pages, 15 figures; References added for V2, version submitted to EPJC
References in corpus (18)
- PYTHIA 6.4 Physics and Manual
- The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
- An Introduction to PYTHIA 8.2
- Implications of CTEQ global analysis for collider observables
- Threshold resummation for squark-antisquark and gluino-pair production at the LHC
- Soft gluon resummation for the production of gluino-gluino and squark-antisquark pairs at the LHC
- Search for direct production of charginos, neutralinos and sleptons in final states with two leptons and missing transverse momentum in pp collisions at sqrt(s) = 8 TeV with the ATLAS detector
- Search for squarks and gluinos with the ATLAS detector in final states with jets and missing transverse momentum using TeV proton--proton collision data
- CheckMATE 2: From the model to the limit
- Search for new phenomena in final states with an energetic jet and large missing transverse momentum in collisions at TeV using the ATLAS detector
- Search for pair-produced third-generation squarks decaying via charm quarks or in compressed supersymmetric scenarios in collisions at TeV with the ATLAS detector
- Search for top squark pair production in final states with one isolated lepton, jets, and missing transverse momentum in 8 TeV pp collisions with the ATLAS detector
- Search for direct top-squark pair production in final states with two leptons in pp collisions at sqrt(s)=8TeV with the ATLAS detector
- Search for squarks and gluinos in final states with jets and missing transverse momentum at 13 TeV with the ATLAS detector
- Search for supersymmetry at sqrt(s)=8 TeV in final states with jets and two same-sign leptons or three leptons with the ATLAS detector
- The BSM-AI project: SUSY-AI - Generalizing LHC limits on Supersymmetry with Machine Learning
- Profile likelihood maps of a 15-dimensional MSSM
- Search for new physics in final states with two opposite-sign, same-flavor leptons, jets, and missing transverse momentum in pp collisions at sqrt(s) = 13 TeV