A sampling algorithm to estimate the effect of fluctuations in particle physics data

TL;DR

This paper introduces a novel sampling algorithm inspired by the Gibbs sampler to estimate the shapes of signal and background distributions in particle physics data, accounting for fluctuations to improve analysis accuracy.

Contribution

It presents a new algorithm that refines control sample templates to incorporate fluctuations, enhancing background subtraction and measurement resolution in particle physics experiments.

Findings

Algorithm successfully estimates distribution shapes from Monte Carlo data.

Incorporates fluctuations into background templates, improving analysis accuracy.

Discusses potential for future development and application.

Abstract

Background properties in experimental particle physics are typically estimated using large data sets. However, different events can exhibit different features because of the quantum mechanical nature of the underlying physics processes. While signal and background fractions in a given data set can be evaluated using a maximum likelihood estimator, the shapes of the corresponding distributions are traditionally obtained using high-statistics control samples, which normally neglects the effect of fluctuations. On the other hand, if it was possible to subtract background using templates that take fluctuations into account, this would be expected to improve the resolution of the observables of interest, and to reduce systematics depending on the analysis. This study is an initial step in this direction. We propose a novel algorithm inspired by the Gibbs sampler that makes it possible to estimate the shapes of signal and background probability density functions from a given collection of particles, using control sample templates as initial conditions and refining them to take into account the effect of fluctuations. Results on Monte Carlo data are presented, and the prospects for future development are discussed.