Effective LHC measurements with matrix elements and machine learning

TL;DR

This paper reviews traditional and modern analysis techniques for LHC data, highlighting how combining matrix element methods with machine learning can improve measurement sensitivity.

Contribution

It introduces new inference methods that integrate matrix element information with machine learning, and discusses the MadMiner package for automated data processing.

Findings

New techniques can substantially improve LHC measurement sensitivity

Combining matrix elements with machine learning enhances likelihood estimation

MadMiner automates data processing for advanced analyses

Abstract

One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies solve this issue, including the traditional histogram approach used in most particle physics analyses, the Matrix Element Method, Optimal Observables, and modern techniques based on neural density estimation. We then discuss powerful new inference methods that use a combination of matrix element information and machine learning to accurately estimate the likelihood function. The MadMiner package automates all necessary data-processing steps. In first studies we find that these new techniques have the potential to substantially improve the sensitivity of the LHC legacy measurements.