Exploring Theory Space with Monte Carlo Reweighting

TL;DR

This paper presents a reweighting method for Monte Carlo simulations that enables efficient exploration of large parameter spaces in new physics models, facilitating collaboration between theorists and experimentalists at the LHC.

Contribution

It introduces a reweighting technique for Monte Carlo events that allows reuse of simulations across different models, improving the efficiency of parameter space scans.

Findings

Reweighting enables efficient exploration of large theory spaces.

Method improves collaboration between theorists and experimentalists.

Facilitates rigorous testing of new physics models at the LHC.

Abstract

Theories of new physics often involve a large number of unknown parameters which need to be scanned. Additionally, a putative signal in a particular channel may be due to a variety of distinct models of new physics. This makes experimental attempts to constrain the parameter space of motivated new physics models with a high degree of generality quite challenging. We describe how the reweighting of events may allow this challenge to be met, as fully simulated Monte Carlo samples generated for arbitrary benchmark models can be effectively re-used. In particular, we suggest procedures that allow more efficient collaboration between theorists and experimentalists in exploring large theory parameter spaces in a rigorous way at the LHC.