A population-based approach to background discrimination in particle physics

TL;DR

This paper introduces a mixture model decomposition method to estimate fluctuations in background distributions in particle physics data, aiming to improve background modeling beyond traditional control sample techniques.

Contribution

A novel mixture model approach that accounts for fluctuations in background distributions directly from data, enhancing background discrimination in particle physics experiments.

Findings

Feasibility demonstrated on Monte Carlo data.

Compared favorably with existing background estimation methods.

Potential for detailed offline analysis at the Large Hadron Collider.

Abstract

Background properties in experimental particle physics are typically estimated using control samples corresponding to large numbers of events. This can provide precise knowledge of average background distributions, but typically does not consider the effect of fluctuations in a data set of interest. A novel approach based on mixture model decomposition is presented as a way to estimate the effect of fluctuations on the shapes of probability distributions in a given data set, with a view to improving on the knowledge of background distributions obtained from control samples. Events are treated as heterogeneous populations comprising particles originating from different processes, and individual particles are mapped to a process of interest on a probabilistic basis. The proposed approach makes it possible to extract from the data information about the effect of fluctuations that would otherwise be lost using traditional methods based on high-statistics control samples. A feasibility study on Monte Carlo is presented, together with a comparison with existing techniques. Finally, the prospects for the development of tools for intensive offline analysis of individual events at the Large Hadron Collider are discussed.