Benchmark Models, Planes, Lines and Points for Future SUSY Searches at the LHC
S. S. AbdusSalam, B. C. Allanach, H. K. Dreiner, J. Ellis, U. Ellwanger, J. Gunion, S. Heinemeyer, M. Kraemer, M. L. Mangano, K. A. Olive, S. Rogerson, L. Roszkowski, M. Schlaffer, G. Weiglein
TL;DR
This work presents a structured framework of SUSY benchmark models and subspaces (planes, lines, points) to standardize and extend the interpretation of LHC searches. By incorporating CMSSM, NUHM, mGMSB, mAMSB, MM-AMSB, p19MSSM, RPV, and NMSSM, the authors provide concrete, testable spectra and signature-topologies that can be updated as data accrue, including relic-density considerations and NLSP phenomenology. They discuss how current SUSY fits shape benchmark choices, and they offer explicit lines and points designed to probe diverse mass hierarchies, decay modes, and experimental signatures while keeping input spectra available (Les Houches format). The framework aims to facilitate cross-model comparisons, detector performance tests, and efficient exploration of higher-mass regions as the LHC advances, while acknowledging that cosmological constraints and alternative DM scenarios may affect the relevance of certain CMSSM-like benchmarks. Overall, the paper delivers a practical, extensible mapping of theoretically motivated SUSY scenarios into actionable experimental benchmarks across a broad model landscape.
Abstract
We define benchmark models for SUSY searches at the LHC, including the CMSSM, NUHM, mGMSB, mAMSB, MM-AMSB and p19MSSM, as well as models with R-parity violation and the NMSSM. Within the parameter spaces of these models, we propose benchmark subspaces, including planes, lines and points along them. The planes may be useful for presenting results of the experimental searches in different SUSY scenarios, while the specific benchmark points may serve for more detailed detector performance tests and comparisons. We also describe algorithms for defining suitable benchmark points along the proposed lines in the parameter spaces, and we define a few benchmark points motivated by recent fits to existing experimental data.
