TMVA - Toolkit for Multivariate Data Analysis

By Andreas Hoecker, P. Speckmayer, J. Stelzer, J. Therhaag, E. Toerne, H. Voss, M. Backes, T. Carli, O. Cohen, A. Christov and 17 more. 2007

1,458 citations recorded by the source corpus.

n 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. They range from rectangular cut optimization using a genetic algorithm and from one- and multidimensional likelihood estimators, over linear and nonlinear discriminants and neural networks, to sophisticated more recent classifiers such as a support vector machine, boosted decision trees and rule ensemble fitting. TMVA manages the simultaneous training, testing, and performance evaluation of all these classifiers with a user-friendly interface, and expedites the application of the trained classifiers to data.

Subfields: Statistics and Probability, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7ae0f1af230b92462b812b92e2ceb4436d0a2f40

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Citations in the corpus

Letter of intent for KM3NeT 2.0 (2016, methodology): “(2) A multivariate analysis based on the BDT from the ROOT TMVA package [41], applied to the preselected events for a more stringent background rejection.”
Determination of the quark coupling strength |Vub| using baryonic decays (2015, methodology): “6: Schematic view of a decision tree taken from [80].”
Study of the spin and parity of the Higgs boson in diboson decays with the ATLAS detector (2015, positive): “The Boosted Decision Tree (BDT) algorithm [17] used to discriminate signals from the Z Z∗ background, described in Sects.”
The Lund jet plane (2018, methodology): “he jet mass and the D 2 observable [13] with = 2 (itself very similar to C 2 [11] for any given jet mass). The jet mass and D 2 are then given as an input to a boosted-decision tree (BDT) in the TMVA [79] package. This pair of observables, used with just a mass cut, not a BDT,11 was found to be clos”
Search for the standard model Higgs boson produced in association with a W or a Z boson and decaying to bottom quarks (2013, methodology): “These limits are extracted by fitting the shape of the output distribution of a boosted-decision-tree (BDT) discriminant [14, 15].”
Searching for exotic particles in high-energy physics with deep learning (2014, methodology): “widely-used TMVA package [22], which provides a standardized implementation of common multi-variate learning techniques and an excellent performance baseline.”
Measurement of Higgs boson production and properties in the WW decay channel with leptonic final states (2013, background): “These uncertainties are assumed to be correlated among processes with identical LO initial states, without considering whether or not they are signal or background processes.”
Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume II: DUNE Physics (2020, methodology): “The software package Toolkit for Multivariate Data Analysis with ROOT (TMVA4) [215] was used with AdaBoost as the boosted algorithm.”