# MadMiner: Machine learning-based inference for particle physics

## TL;DR

MadMiner is a Python toolkit that enhances particle physics analyses at the LHC by integrating machine learning with matrix element methods, improving sensitivity to new physics without simplifying data or physics models.

## Contribution

It introduces MadMiner, a versatile Python module that simplifies implementing advanced multivariate inference techniques in particle physics analyses.

## Key findings

- Substantially increased sensitivity to new physics in ttH production.
- Supports a wide range of physics processes and models.
- Integrates with existing simulation tools like MadGraph, Pythia, and Delphes.

## Abstract

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.