The Simplified Likelihood Framework

TL;DR

The paper introduces a systematic simplified likelihood framework for experimental data, especially from LHC, using the Central Limit Theorem with next-to-leading terms, validated through realistic analysis and compatible with HepData.

Contribution

It develops a new approximation scheme for experimental likelihoods that accounts for asymmetries and provides an efficient method to extract parameters from simulations.

Findings

Validated the simplified likelihood approach with LHC-like analysis

Demonstrated efficient parameter extraction from Monte Carlo simulations

Showed compatibility with HepData format for data sharing

Abstract

We discuss the simplified likelihood framework as a systematic approximation scheme for experimental likelihoods such as those originating from LHC experiments. We develop the simplified likelihood from the Central Limit Theorem keeping the next-to-leading term in the large $N$ expansion to correctly account for asymmetries. Moreover, we present an efficient method to compute the parameters of the simplified likelihood from Monte Carlo simulations. The approach is validated using a realistic LHC-like analysis, and the limits of the approximation are explored. Finally, we discuss how the simplified likelihood data can be conveniently released in the HepData error source format and automatically built from it, making this framework a convenient tool to transmit realistic experimental likelihoods to the community.