Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes

17 Sep 2017  ·  Meghan Frate, Kyle Cranmer, Saarik Kalia, Alexander Vandenberg-Rodes, Daniel Whiteson ·

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.

PDF Abstract
No code implementations yet. Submit your code now


Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology