Log Gaussian Cox process background modeling in high energy physics

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 (LGCPs) 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.