MadMiner: Machine Learning-Based Inference for Particle Physics

• DOI: 10.1007/s41781-020-0035-2
• Publication Date: 2020-01-18T05:03:51Z
• Authors: Johann Brehmer; Felix Kling; Irina Espejo; Kyle Cranmer
• Keywords: Python; Leverage (statistics)

MadMiner is available at https://github.com/diana-hep/madminer . v2: improved text, fixed typos, better colors, added references<br/>Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods. Recently, a powerful family of multivariate inference techniques that leverage both matrix element information and machine learning has been developed. This approach neither requires the reduction of high-dimensional data to summary statistics nor any simplifications to the underlying physics or detector response. In this paper we introduce MadMiner, a Python module that streamlines the steps involved in this procedure. Wrapping around MadGraph5_aMC and Pythia 8, it supports almost any physics process and model. To aid phenomenological studies, the tool also wraps around Delphes 3, though it is extendable to a full Geant4-based detector simulation. We demonstrate the use of MadMiner in an example analysis of dimension-six operators in ttH production, finding that the new techniques substantially increase the sensitivity to new physics.<br/>