Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes

2017/09/17 by Meghan Frate, Frate, Meghan, K. Cranmer +7 · 6 citations
Physics and Astronomy · Computer Science · #Scientific Research and Discoveries #Gaussian Processes and Bayesian Inference #Computational Physics and Python Applications

paper · pdf · doi:10.48550/arxiv.1709.05681

Abstract

We describe a procedure for constructing a model of a smooth data spectrum using Gaussian processes rather than the historical parametric description. This approach considers a fuller space of possible functions, is robust at increasing luminosity, and allows us to incorporate our understanding of the underlying physics. We demonstrate the application of this approach to modeling the background to searches for dijet resonances at the Large Hadron Collider and describe how the approach can be used in the search for generic localized signals.

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