1 million cMSSM parameter space points with low-energy predictions from SPheno and MicrOMEGAs

This dataset was produced and used in the paper Symbolically Regressing Beyond the Standard Model Physics. The code used to generate and to analyse these data can be found here.

The dataset specifications:

• Randomly sampled 1 million points of the cMSSM parameter space and respective low-energy observables.
• Low-energy observables computed using using `SPheno` and `MicrOMEGAs`.
  • Only points that produced `SPheno` output and neutral LSP are processed by `MicrOMEGAs`.
  • The dataset includes all points, even if they are "unphysical", i.e. points without `SPheno` output or neutral LSP. In the paper, this was used to train a classifier to filter out "unphysical" points.
• The columns are
  • 'm0', 'm12', 'A0', 'tanb': the four physical parameters of the theory sampled in the priori
    • 'm0': [0, 10] TeV
    • 'm12': [0, 10] TeV
    • 'A0': [-60,60] TeV
    • 'tanb': [1.5,50]
    • The sign of the 'mu' parameter was fixed to positive (+1)
  • 'idx': an utility identifier used during generation, can/should be ignored
  • Flattened `SPheno` outputs. These are obtained by reading the resulting slha spectrum file outputted by SPheno and flatten the blocks. For example from the 'MINPAR' block, the key-value pairs are given by the columns  'MINPAR_1', 'MINPAR_2',  'MINPAR_3',  'MINPAR_4', 'MINPAR_5', and likewise for all blocks in the slha file.
  • `MicrOMEGAs` outputs. These inlcude: 'dm_Omega', 'dm_spin', 'dm_candidate`, `mo_output`, `dm_c_{bino,wino,higgsino1,higgsino2}`, which are, respectively: dark matter relic density value, dark matter candidate spin, dark matter candidate, the whole `MicrOMEGAs` output, and the coefficient of  `{bino,wino,higgsino1,higgsino2}` components of the dark matter state.

Versions:

• SPheno 4.0.5, with a patch to output a warning when the LSP is charged. This version can be found here.
• MicrOMEGAs 5.3.41, with the MSSM model adapted for low-scale slha inputs.

The datasets are provided in Apache `parquet` format. In order to read them using `pandas`, an installation with the optional flag `[parquet]` should be used. Alternatively, one can use `pyarrow`.