Simulation Based Inference for Efficient Theory Space Sampling: an Application to Supersymmetric Explanations of the Anomalous Muon (g-2)
Authors: Logan Morrison, Stefano Profumo, Nolan Smyth, and John Tamanas
In our paper, we introduce the simulation-based inference (SBI) framework to the problem of sampling from experimentally-constrained theory spaces.
In this repository, you'll find SBI applications to cMSSM and pMSSM parameter space samplings.
Required python packages are listed in environment.yml. To create a Conda environment with these dependencies use the following command:
conda env create -f environment.yml
Additionally, this package relies on:
- jax-based sbi package available here: https://github.com/jtamanas/LBI
- python-wrapped micromegas package available here: https://github.com/LoganAMorrison/pymicromegas