Published June 30, 2023
| Version v1
Dataset
Open
EW-ino scan points from "SModelS v2.3: enabling global likelihood analyses" paper
Authors/Creators
- 1. Univ. Grenoble Alpes, CNRS, Grenoble INP, LPSC-IN2P3, Grenoble, France
- 2. Centro de Ciencias Naturais e Humanas, UFABC, Santo Andre, Brazil
- 3. HEPHY/OEAW and University of Vienna, Vienna, Austria
Description
Input SLHA and SModelS output (.smodels and .py) files from the paper "SModelS v2.3: enabling global likelihood analyses". The dataset comprises 18557 electroweak-ino scan points and can be used to reproduce all the plots presented in the paper.
- ewino_slha.tar.gz : input SLHA files including mass spectra, decay tables and cross sections
- ewino_smodels_v23_combSRs.tar.gz : SModelS v2.3 output with combineSRs=True and combineAnas = ATLAS-SUSY-2018-41,CMS-SUS-21-002 (primary v2.3 results used in section 4, Figs. 2-5)
- ewino_smodels_v23_bestSR.tar.gz : SModelS v2.3 output with combineSRs=False and combineAnas = ATLAS-SUSY-2018-41,CMS-SUS-21-002 (used only in Fig. 2)
- ewino_smodels_v21.tar.gz : SModelS v2.1 output with combineSRs=False (used only in Fig. 2)
Notes
Funded in part by: IN2P3 master project ``Theorie - BSMGA'', French Agence Nationale de la Recherche (ANR) under grant ANR-21-CE31-0023 (PRCI SLDNP), Austrian Science Fund (FWF) under grant number I5767-N,
Initiatives de Recherche a Grenoble Alpes (IRGA) ANR-15-IDEX-02 project no. G7H-IRG21B26 (APM@LHC), FAPESP grant no. 2018/25225-9 and 2021/01089-1.
Files
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Additional details
Funding
- FWF Austrian Science Fund
- Statistically Learning Dispersed New Physics at the LHC I 5767
- Agence Nationale de la Recherche
- SLDNP - Statistically Learning Dispersed New Physics at the LHC ANR-21-CE31-0023