In our GitHub repository we provide a set of tutorials that are probably a good way to get started with MadMiner.
As a starting point, we recommend to look at a tutorial based on a toy example. It demonstrates inference with MadMinier without spending much time on the more technical steps of running the simulation.
We then provide two sets of tutorials for the same real-world particle physics process. The difference between them is that the parton-level tutorial only requires running MadGraph. Instead of a proper shower and detector simulation, we describe detector effects through simple smearing functions. This reduces the runtime of the scripts quite a bit. In the Delphes tutorial, we finally switch to Pythia and Delphes; this tutorial is probably best suited as a starting point for phenomenological research projects. In most other aspects, the two tutorials are identical.
Other provided examples show MadMiner in action in different processes.
madminer.corecontains the functions to set up the process, parameter space, morphing, and to steer MadGraph and Pythia.
madminer.delphescontain two example implementations of a detector simulation and observable calculation. This part can easily be swapped out depending on the use case.
madminer.sampling, train and test samples for the machine learning part are generated and augmented with the joint score and joint ratio.
madminer.mlcontains an implementation of the machine learning part. The user can train and evaluate estimators for the likelihood ratio or score.
madminer.fisherinformationcontains functions to calculate the Fisher information, both on parton level or detector level, in the full process, individual observables, or the total cross section.
The madminer API is documented on here as well, just look through the pages linked on the left.