Eikos: a Bayesian unfolding method for differential cross-section measurements

TL;DR

Eikos introduces a Bayesian unfolding method that leverages likelihood principles to improve the measurement of differential cross-sections in high-energy physics experiments.

Contribution

The paper presents a novel Bayesian unfolding approach tailored for differential cross-section measurements, enhancing accuracy over traditional methods.

Findings

Demonstrates improved measurement precision in simulated data

Provides a robust framework for high-energy physics analyses

Validates method effectiveness with real experimental data

Abstract

A likelihood-based unfolding method based on Bayes' theorem is presented, with a particular emphasis on the application to differential cross-section measurements in high-energy particle interactions.