Density Estimation Trees in High Energy Physics
Lucio Anderlini
TL;DR
Density Estimation Trees offer a valuable method for analyzing complex multidimensional data in high energy physics, utilizing a self-optimizing algorithm based on kernel density estimation.
Contribution
The paper introduces a novel application of Density Estimation Trees with a self-optimization technique for high energy physics data analysis.
Findings
Effective in modeling multi-modal data distributions
Improves exploratory data analysis in high energy physics
Demonstrates practical applications in the field
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.
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Taxonomy
TopicsComputational Physics and Python Applications · Generative Adversarial Networks and Image Synthesis · Data Analysis with R