10–14 Oct 2016
San Francisco Marriott Marquis
America/Los_Angeles timezone

Divergence techniques in high energy physics

11 Oct 2016, 15:30
1h 15m
San Francisco Marriott Marquis

San Francisco Marriott Marquis

Poster Track 5: Software Development Posters A / Break

Description

Binary decision trees are a widely used tool for supervised classification of high-dimensional data, for example among particle physicists. We present our proposal of the supervised binary divergence decision tree with nested separation method based on kernel density estimation. A key insight we provide is the clustering driven only by a few selected physical variables. The proper selection consists of the variables achieving the maximal divergence measure between two different subclasses of data. Further we apply our method to Monte Carlo data set from the particle accelerator Tevatron at the D0 experiment in Fermilab. We also introduce the modification of statistical tests applicable to weighted data sets in order to test homogeneity of the Monte Carlo simulation and real data.

Primary Keyword (Mandatory) Algorithms
Secondary Keyword (Optional) Analysis tools and techniques
Tertiary Keyword (Optional) Artificial intelligence/Machine learning

Primary author

Petr Bouř (FNSPE CTU Prague)

Presentation materials