Log Gaussian Cox Process Background Modeling in High Energy Physics
This paper introduces a novel Log Gaussian Cox Process (LGCP) method for modeling smooth backgrounds in high energy physics that minimizes assumptions about the underlying shape by utilizing a Gaussian process for the intensity function and Markov Chain Monte Carlo for optimization, demonstrating its effectiveness through synthetic experiments against traditional analytic functional forms.