Dataset Open Access

Supplementary Data: A global fit of the MSSM with GAMBIT (arXiv:1705.07917)

The GAMBIT Collaboration

Supplementary Data

A global fit of the MSSM with GAMBIT
arXiv:1705.07917

The files in this record contain data for the MSSM7 model considered in the GAMBIT “Round 1” weak-scale SUSY paper.

The files consist of

  • A number of YAML files corresponding to different sets of sampling parameters and/or priors
  • MSSM7.yaml, a YAML file used for postprocessing
  • StandardModel_SLHA2_scan.yaml, a universal YAML fragment included from other YAML files
  • StandardModel_SLHA2_postprocessing.yaml, a YAML fragment included from MSSM7.yaml
  • A final hdf5 file, containing the combined results of all sampling runs
  • An example pip file, for producing plots from the hdf5 file using pippi
  • gambit_preamble.py, a collection of python functions used for in-line data processing in the pip file
  • SLHA1 and SLHA2 files for the best-fit point in each subregion of the fit.  These can found inside the tarball best_fits_SLHA.tar.gz.

The different YAML files corresponding to different samplers and/or priors follow the naming scheme MSSM7_[scanner]_[prior]_[slice]_[special].yaml , where

  • scanner = Diver, MN
  • prior = log, flat
  • slice = nM2, pM2, Afunnel, hZfunnel, sqcoann, slcoann (positive or negative M2, A/H funnel, h/Z funnel, squark co-annihilation, slepton co-annihilation)
  • special = jDE, [blank] (used pure jDE, or used the default lambdajDE)

A few caveats to keep in mind:

  1. The final hdf5 results file included here was generated in the following way:

    • carry out initial runs using YAML files following the naming scheme above
    • combine the resulting hdf5 output files into a single file, using
      gambit/Printers/scripts/combine_hdf5.py
    • postprocess the samples to remove all points more than 5 sigma from the current best fit, using MSSM7_strip.yaml
    • postprocess the samples to include a new likelihood term for LHC Run II searches, and to recompute the FlavBit likelihoods (these were buggy in a pre-release version of GAMBIT), using MSSM7.yaml .
  2. It is not necessary to repeat the steps listed in point 1 when running new scans; the LHC Run II likelihoods can be included in the original YAML file, so that no postprocessing step is required.

  3. The YAML files that we give here are updated compared to the ones that we used when generating the hdf5 file, in order to match the set of available options in the release version of GAMBIT 1.0.0. The included physics and numerics are however identical.

  4. The YAML files are designed to work with the tagged release of GAMBIT 1.0.0, and the pip file is tested with pippi 2.0, commit 2ab061a8. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).

  5. The pip file is an example only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please don’t expect the same level of polish as for files provided here or in the GAMBIT repo.

v2 adds SLHA1 and SLHA2 benchmark files for the best-fit points in each region of each model.
Files (35.6 GB)
Name Size
best_fits_SLHA.tar.gz md5:a2943a2828b820047c3da1e96c618beb 116.0 kB Download
gambit_preamble.py md5:836f4aae006b11761523f5b5f92b78aa 1.5 kB Download
MSSM7.hdf5.tar.gz md5:c19061a02a43a02c32bec648e328bda4 35.6 GB Download
MSSM7.pip md5:8ac032eb95a9cd6081a9454bcc6f02f0 14.5 kB Download
MSSM7.yaml md5:dada82f479f529703bffdf22e7248889 7.6 kB Download
MSSM7_Diver_flat_nM2.yaml md5:86d17a69061712e67e7c215b1b1e8fe7 11.7 kB Download
MSSM7_Diver_flat_nM2_jDE.yaml md5:9bf9f2c00b9a6e57f2c54b744f250dda 11.8 kB Download
MSSM7_Diver_flat_pM2.yaml md5:08dbdf3cb8d559b9e6c25ca14c4467fa 11.7 kB Download
MSSM7_Diver_flat_pM2_jDE.yaml md5:3b3b830b2105e8503f4430d3d70f01dc 11.8 kB Download
MSSM7_Diver_log_Afunnel_jDE.yaml md5:7cd9e5b629d15385a3a5f7266a7559b5 12.2 kB Download
MSSM7_Diver_log_hZfunnel_jDE.yaml md5:887f7c920b1067037d3db0bbfffed841 12.0 kB Download
MSSM7_Diver_log_nM2.yaml md5:5a08d8c04af32ed2ad66395c7f361e9b 11.9 kB Download
MSSM7_Diver_log_nM2_jDE.yaml md5:9518e08a1f3d08ef8b71b70ba6fc72b6 11.9 kB Download
MSSM7_Diver_log_pM2.yaml md5:0c04595dee4c13dd355fdaff307f33ab 11.9 kB Download
MSSM7_Diver_log_pM2_jDE.yaml md5:aea983d5f9328b953229a5c76463cef1 11.9 kB Download
MSSM7_Diver_log_slcoann_jDE.yaml md5:d6ca37399cd2073dd89026fc6f9f57c7 12.3 kB Download
MSSM7_Diver_log_sqcoann_jDE.yaml md5:05048b658cf31b99eb3cdf7f4ef680d8 12.3 kB Download
MSSM7_MN_flat_nM2.yaml md5:1d36fb98a3e591753e56df4e595b9a78 11.6 kB Download
MSSM7_MN_flat_pM2.yaml md5:539f4995ace96e064ddc675a9c204a21 11.6 kB Download
MSSM7_MN_log_nM2.yaml md5:2520a2a417eb114cdc44e302f58e8f88 11.9 kB Download
MSSM7_MN_log_pM2.yaml md5:e54c3812e6ea5a58982f03bc49a5f8a5 11.9 kB Download
README_MSSM7.md md5:1bda59e7963cf5980654109f6099b050 3.4 kB Download
StandardModel_SLHA2_postprocessing.yaml md5:84fa3f0c3a2f9ec3cab8179138dcf7d1 948 Bytes Download
StandardModel_SLHA2_scan.yaml md5:0dd9947f80c9c2c441d19df1057fc9bf 3.0 kB Download

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