Iterative subtraction method for Feature Ranking

Abstract

Training features used to analyse physical processes are often highly correlated and determining which ones are most important for the classification is a non-trivial tasks. For the use case of a search for a top-quark pair produced in association with a Higgs boson decaying to bottom-quarks at the LHC, we compare feature ranking methods for a classification BDT. Ranking methods, such as the BDT Selection Frequency commonly used in High Energy Physics and the Permutational Performance, are compared with the computationally expense Iterative Addition and Iterative Removal procedures, while the latter was found to be the most performant.

References in corpus (5)

• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Measurements of $b$-jet tagging efficiency with the ATLAS detector using $t\bar{t}$ events at $\sqrt{s}=13$ TeV
• Performance of top-quark and $W$-boson tagging with ATLAS in Run 2 of the LHC
• Search for the Standard Model Higgs boson produced in association with top quarks and decaying into a $b\bar{b}$ pair in $pp$ collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector
• HepSim: a repository with predictions for high-energy physics experiments