Density Estimation Trees in High Energy Physics

Abstract

Density Estimation Trees can play an important role in exploratory data analysis for multidimensional, multi-modal data models of large samples. I briefly discuss the algorithm, a self-optimization technique based on kernel density estimation, and some applications in High Energy Physics.

References in corpus (1)

• Measurement of the Higgs boson mass from the $H\to \gamma \gamma$ and $H\to Z{Z}^{\ast }\to 4\ell$ channels with the ATLAS detector using 25 fb${}^{-1}$ of $pp$ collision data