ScanLHA 0.3

Perform parameter scans with HEP tools that use (not only) (S)LHA in- and output.

ScanLHA

Perform parameter scans with HEP tools that use (not only) (S)LHA in- and output.

Use Case

Typical tool-chains in high-energy physics (HEP) are the pass-through of one SLHA-input file from spectrum generators, e.g. SPheno, to further HEP tools like HiggsBounds and/or micrOmegas which themself return SLHA-output files.
To provide correct input for the various tools between the different steps as well as to enable further processing (e.g. plotting) the SLHA format needs to be parsed and stored in a storage-efficient tabular format.
In a phenomenological study, most of the physical parameters as well as config flags within a considered scenario are kept constant while only a few O(1-20) parameters are varied in different ways (grid- or randomly scanned, more sophisticated scanning techniques are planned in the future).
Due to the large combinatorics, the scan may be done in parallel and even be distributed over different machines (using e.g. Sun Grid Engine, the usage of dask.distributed is planned for the future).
The outcome is one (or multiple) HDF files (to be merged) that may undergo further editing before the results are visualized in 2D or 3D scatter plots.

Installing

    pip3 install ScanLHA

Executables

• ScanLHA - Perform (S)LHA scan/s and save to HDF file/s.
• PlotLHA - Plot ScanLHA results from HDF file/s.
• EditLHA - Interactively load/edit/save/plot HDF file/s.
• MergeLHA - Merge multiple HDF files into one file.

The executables ScanLHA and PlotLHA take a YAML input config file.

Scanning

The config YAML file must contain two dictionaries runner and blocks controlling the type of scan/tools to be used as well as the SLHA blocks that have to be present/scanned in the input file.
In order to simplify the distribution of similar scans through grid-computing software, it is possible to declare argument SLHA entries. The value/scanrange of these entries can be set from within command line arguments.

A basic config.yml file to run SPheno and HiggsBounds may look like

    ---
    runner:
      binaries:
        - ['/bin/SPhenoMSSM', '{input_file}', '{output_file}']
        - ['./HiggsBounds', 'LandH', 'SLHA', '3', '0', '{output_file}']
      tmpfs: /dev/shm/slha
      keep_log: true
      timeout: 90
      scantype: random
      numparas: 50000
      constraints: # Higgs mass constraint
        - "result['MASS']['values']['25']<127.09"
        - "result['MASS']['values']['25']>123.09"
    blocks:
        - block: MINPAR
          lines:
              - parameter: 'MSUSY'
                latex: '$M_{SUSY}$ (GeV)'
                id: 1
                random: [500,3500]
              - parameter: 'TanBeta'
                latex: '$\tan\beta$'
                argument: 'value'
              - parameter: 'mu'
                latex: '$\mu$ (GeV)'
                id: 2
                value: 300

Where the id is the SLHA-id of the parameter parameter in the block block which either takes the constant value value, is randomly chosen or scaned in a grid.
The presence of the new command line argument TanBeta may be verified with ScanLHA config.yml --help.
A scan that runs the SPheno->HiggsBounds chain in 2 parallel threads is started with ScanLHA config.yml -p 2 --TanBeta 4 scantanbeta4.h5 (by default os.cpucount() is used for -p).
For this purpose, 2 copies of the binaries are stored in 2 randomly named directories in runner['tmpfs'] (default: /dev/shm/) where the input and output files are generated.
Alternatively one may specify values: [1, 2, 10] for the line TanBeta instead of argument or even scan: [1, 50, 50] to scan over TanBeta (from 1 to 50 in 50 steps for each random value of MSUSY) and save the result into one single file (likewise, the argument option can be set to scan or random and according numbers may be provided from the command line). Grid scans can have a distribution attribute equaling linear,log,geom,arange,uniform or normal.

The executables in runner['binaries'] are run subsequential for each parameter point using given arguments. For each point a randomly named {input_file} is generated and may be passed as argument to the executables. Likewise, the {output_file} is supposed to be written by the executables and eventually parsed afterwards. One may also make direct use of Python (or C-Python) implementations instead of using executables by implementing a runner module.

Plotting

The config.yml used for the scan may also contain a scatterplot dictionary (but can also be contained in a separate file).

Plotting capabilities are:

• Automatically uses the latex attribute of specified LHA blocks for labels.
• Fields for x/y/z axes can be specified by either BLOCKNAME.values.LHAID or the specified parameter attribute.
• New fields to plot can be computed using existing fields saved in DATA
• Optional constraints on the different fields may be specified using PDATA
• Various options can be passed to matplotlibs legend, scatter, colorbar functions.
• Optional ticks, textboxes, legend-position, colors, etc... can be set manually.

The scatterplot dict must contain a conf dict that specifies at least the HDF datafile to load. Additionaly, defaults for x/y/z axes or other plot configs may be set and need not to be repeated in the plot definitions (but can be overwritten). In addition, the plots key contains a list of dicts that specify the filename and data to plot.

An example configuration may look like

    ---
    scatterplot:
      conf:
        datafile: "mssm.h5"
        newfields:
          TanBeta: "DATA['HMIX.values.2'].apply(abs).apply(tan)"
                   # the string is passed to eval
        constraints:
          - "PDATA['TREELEVELUNITARITYwTRILINEARS.values.1']<0.5"
          # enforces unitarity on the sample
      plots:
          - filename: "mssm_TanBetaMSUSYmH.png"
            # one scatterplot
            y-axis: {field: TanBeta, label: '$\tan\beta$'}
            x-axis:
              field: MSUSY
              label: "$m_{SUSY}$ (TeV)$"
              lognorm: True
              ticks:
                - [1000,2000,3000,4000]
                - ['$1$','$2','$3','$4$']
            z-axis:
              field: MASS.values.25
              colorbar: True
              label: "$m_h$ (GeV)"
            alpha: 0.8
            textbox: {x: 0.9, y: 0.3, text: 'some info'}
          - filename: "mssm_mhiggs.png"
            # multiple lines in one plot with legend
            constraints: [] # ignore all global constraints
            x-axis:
              field: MSUSY,
              label: 'Massparameter (GeV)'
            y-axis:
              lognorm: True,
              label: '$m_{SUSY}$ (GeV)'
            plots:
                - y-axis: MASS.values.25
                  color: red
                  label: '$m_{h_1}$'
                - y-axis: MASS.values.26
                  color: green
                  label: '$m_{h_2}$'
                - y-axis: MASS.values.35
                  color: blue
                  label: '$m_{A}$'

Editing and Merging

See EditLHA --help and MergeLHA --help.

Scanning with other (non-)SLHA Tools

See the API docs of the runner module.

Related Tools

• xBIT
• SSP/pySSP
• ask me if you wish to be listed here