Stochastic resolution of the LHC inverse problem

TL;DR

This paper presents a Bayesian approach to the LHC inverse problem, demonstrating that combining LHC data with other measurements allows for a significant reduction in the complexity of inferring theoretical parameters.

Contribution

It clarifies the Bayesian relationship between theory parameters and experimental signatures and introduces a simple likelihood analysis to effectively solve the inverse problem.

Findings

Likelihood analysis reduces the inverse problem complexity

Combining diverse data sources enhances parameter inference

Approach is robust, economical, and extendable

Abstract

In this work the LHC inverse problem is quantified in the Bayesian context by clarifying the relation between the mapping from the theory parameter space to experimental signature space and the inverse map. We demonstrate that, after complementing the LHC data by existing astrophysical, collider, and low energy measurements, a simple likelihood analysis is able to significantly reduce the inverse problem. The presented approach offers a robust, economic, and extendable way to extract theoretical parameters from the LHC, and other experimental, data.