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mario-mapyde v0.2.0

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Docker Images

Docker images are made available in our container registry.

docker pull ghcr.io/scipp-atlas/mario-mapyde/madgraph
docker pull ghcr.io/scipp-atlas/mario-mapyde/delphes
docker pull ghcr.io/scipp-atlas/mario-mapyde/pyplotting

If you want to run on a machine with an NVidia GPU and use it for limit setting with pyhf, then there's a container for that too:

docker pull ghcr.io/scipp-atlas/mario-mapyde/pyplotting-cuda

Running

There are a few layers of scripts to factorize the different tasks. A typical pipeline will look like:

  1. Call test/wrapper_mgpy.sh to run MadGraph+Pythia and produce a .hepmc file. The script takes options to specify things like:
    • proc/param/run cards for MadGraph
      • includes specifying particle masses, and
    • any kinematic cuts to apply at parton level
    • pythia card
    • center of mass energy
    • number of cores to use for MadGraph and Pythia
  2. Call test/wrapper_delphes.sh to run Delphes, which is a parameterized detector simulation. The output is a ROOT file. The script takes arguments to specify things like:
    • Delphes card
  3. Call something like test/wrapper_ana.sh to analyze the Delphes output. Note that this script can run user-specified code:
  4. If you want to run limits, then there are two additional steps:
    1. Run test/wrapper_SimpleAnalysis.sh to analyze the output of Delphes2SA.py and make inputs for limit setting
    2. Run test/wrapper_pyhf.sh to plug the results from SimpleAnalysis into the public likelihood.

Each job gets its own ${tag}, which is used to tell the various steps in the pipeline which data to operate on.

For an example of a full pipeline, see run_VBFSUSY_standalone, which itself takes various options to help steer the work.

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