Log Gaussian Cox Process Background Modeling in High Energy Physics

TL;DR

This paper introduces a novel LGCP-based method for modeling smooth backgrounds in high energy physics, reducing assumptions on shape and improving flexibility over traditional functional forms.

Contribution

The paper presents a new LGCP-based approach for background modeling that minimizes assumptions and employs MCMC for hyperparameter optimization.

Findings

LGCP method effectively models smooth backgrounds with minimal assumptions.

Synthetic experiments show LGCP outperforms traditional functional form fitting.

The approach offers a flexible alternative for background estimation in physics analyses.

Abstract

Background modeling is one of the most critical components in high energy physics data analyses, and for smooth backgrounds it is often performed by fitting using an analytic functional form. In this paper a novel method based on Log Gaussian Cox Processes (LGCP) is introduced to model smooth backgrounds while making minimal assumptions on the underlying shape. In LGCP, samples are assumed to be drawn from a non-homogeneous Poisson process, with an intensity function drawn from a Gaussian process. Markov Chain Monte Carlo is used for optimizing the hyper parameters and drawing the final fit for the background estimate from the posterior. Synthetic experiments comparing background modeling from functional forms and the LGCP are used to compare the different methods.