New Machine Learning Developments in ROOT/TMVA

¹ CERN
² University of Antioquia
³ Metropolitan Institute of Technology
⁴ University of Florida
⁵ EPFL
⁶ Lulea University of Technology
⁷ Karlsruhe Institute of Technology
⁸ ETH Zurich

^* e-mail: kim.albertsson{at}cern.ch

Published online: 17 September 2019

Abstract

The Toolkit for Multivariate Analysis, TMVA, the machine learning package integrated into the ROOT data analysis framework, has recently seen improvements to its deep learning module, parallelisation of multivariate methods and cross validation. Performance benchmarks on datasets from high-energy physics are presented with a particular focus on the new deep learning module which contains robust fully-connected, convolutional and recurrent deep neural networks implemented on CPU and GPU architectures. Both dense and convo-lutional layers are shown to be competitive on small-scale networks suitable for high-level physics analyses in both training and in single-event evaluation. Par-allelisation efforts show an asymptotical 3-fold reduction in boosted decision tree training time while the cross validation implementation shows significant speed up with parallel fold evaluation.