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TMVA - Toolkit for Multivariate Data Analysis

15 Pith papers cite this work. Polarity classification is still indexing.

15 Pith papers citing it
abstract

In high-energy physics, with the search for ever smaller signals in ever larger data sets, it has become essential to extract a maximum of the available information from the data. Multivariate classification methods based on machine learning techniques have become a fundamental ingredient to most analyses. Also the multivariate classifiers themselves have significantly evolved in recent years. Statisticians have found new ways to tune and to combine classifiers to further gain in performance. Integrated into the analysis framework ROOT, TMVA is a toolkit which hosts a large variety of multivariate classification algorithms. Training, testing, performance evaluation and application of all available classifiers is carried out simultaneously via user-friendly interfaces. With version 4, TMVA has been extended to multivariate regression of a real-valued target vector. Regression is invoked through the same user interfaces as classification. TMVA 4 also features more flexible data handling allowing one to arbitrarily form combined MVA methods. A generalised boosting method is the first realisation benefiting from the new framework.

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representative citing papers

First evidence of the decay $B^+\to\pi^+ e^+ e^-$

hep-ex · 2026-04-29 · conditional · novelty 8.0 · 2 refs

LHCb observes 3.2 sigma evidence for B+ -> pi+ e+ e- with measured branching fraction (2.4 +0.9 -0.8 +0.4 -0.2) x 10^-8, consistent with Standard Model.

Study of the $B^0 \to \Lambda_c^+ \bar{\Lambda}_c^- K_S^0$ decay

hep-ex · 2026-04-16 · unverdicted · novelty 7.0

Relative branching fraction B(B0 → Λc+ Λc- KS0)/B(B+ → Λc+ Λc- K+) measured as 0.53 ± 0.05 ± 0.05 with 3.9σ evidence for Ξc(2923)+ and Ξc(2939)+ resonances consistent with isospin partners.

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