cMSSM parameter space points generated with SPheno and micrOMEGAS

These two datasets were produced to be used in two lectures on Machine Learning for SUSY Model Building taught in pre-SUSY 2023 summer school in Southampton. The code used to generate and to analyse these data can be found here.

The datasets are as following:

• 1 million points generated using SPheno only (so no Dark Matter relic density) for the cMSSM with the physical parameters randomly sampled from the table bellow. The columns are
  • 'm0', 'm12', 'A0', 'tanb': the four physical parameters of the theory
  • 'idx': an utility identifier used during generation, can/should be ignored
  • The 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.
• 10 thousand points generated using SPheno, and which spectrum outputs was then fed to micrOMEGAS (MSSM model configured to accept low-scale slha files as input), with the physical parameters randomly sampled from the same table bellow. The columns are:
  • The same as above, in addition to
  •  'Omega', 'dm_spin',  'dm_mass' obtained from the micrOMEGAS output, representing Dark Matter relic density, Dark Matter spin, Dark Matter mass, respectively.

The full list of columns can be seen in `column_names.txt` file.

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`.