A Bayesian approach to evaluate confidence intervals in counting experiments with background

TL;DR

This paper introduces a Bayesian method for calculating confidence intervals in counting experiments with background noise, focusing on Poisson statistics to improve upper limit estimations.

Contribution

It presents a novel Bayesian approach specifically designed for evaluating confidence intervals in experiments with Poisson-distributed signal and background fluctuations.

Findings

Effective in calculating Bayesian confidence intervals

Improves upper limit estimations in counting experiments

Applicable to experiments with Poisson noise

Abstract

In this paper we propose a procedure to evaluate Bayesian confidence intervals in counting experiments where both signal and background fluctuations are described by the Poisson statistics. The results obtained when the method is applied to the calculation of upper limits will also be illustrated.