Review of High-Quality Random Number Generators

This is a review of pseudorandom number generators (RNG's) of the highest quality, suitable for use in the most demanding Monte Carlo calculations. All the RNG's we recommend here are based on the Kolmogorov-Anosov theory of mixing in classical mechanical systems, which guarantees under certain conditions and in certain asymptotic limits, that points on the trajectories of these systems can be used to produce random number sequences of exceptional quality. We outline this theory of mixing and establish criteria for deciding which RNG's are sufficiently good approximations to the ideal mathematical systems that guarantee highest quality. The well-known RANLUX (at highest luxury level) and its recent variant RANLUX++ are seen to meet our criteria, and some of the proposed versions of MIXMAX can be modified easily to meet the same criteria.Comment: 21 pages, 4 figure

Variable Definition and Independent Components

In the causal modelling literature, it is well known that “ill-defined” variables may give rise to “ambiguous manipulations” (Spirtes and Scheines, 2004). Here, we illustrate how ill-defined variables may also induce mistakes in causal inference when standard causal search methods are applied (Spirtes et al., 2000; Pearl, 2009). To address the problem, we introduce a representation framework, which exploits an independent component representation of the data, and demonstrate its potential for detecting ill-defined variables and avoiding mistaken causal inferences

Physics data management tools: computational evolutions and benchmarks

The development of a package for the management of physics data is described: its design, implementation and computational benchmarks. This package improves the data management tools originally developed for Geant4 physics models based on the EADL, EEDL and EPDL97 data libraries. The implementation exploits recent evolutions of the C++ libraries appearing in the C++0x draft, which are intended for inclusion in the next C++ ISO Standard. The new tools improve the computational performance of physics data management.Comment: 6 pages, to appear in proceedings of the Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo 2010 (SNA + MC2010

Inference of ML models on Intel GPUs with SYCL and Intel OneAPI using SOFIE

TMVA provides a fast inference system that takes an ONNX model as input and produces compilationready standalone C++ scripts as output which can be compiled and executed on CPU architectures. The idea of this project is to extend this capability to generate from the TMVA SOFIE model representation code that can be run also on Intel GPUs using both SYCL and Intel OneAPI libraries. These will allow for a more efficient evaluation of these models on Intel accelerator hardware

ROOT Statistical Software

Advanced mathematical and statistical computational methods are required by the LHC experiments for analyzing their data. Some of these methods are provided by the ROOT project, a C++ Object Oriented framework for large scale data handling applications. We review the current mathematical and statistical classes present in ROOT, emphasizing the recent developments

Machine Learning Developments in ROOT

ROOT is a software framework for large-scale data analysis that provides basic and advanced statistical methods used by high-energy physics experiments. It includes machine learning tools from the ROOT-integrated Toolkit for Multivariate Analysis (TMVA). We present several recent developments in TMVA, including a new modular design, new algorithms for pre-processing, cross-validation, hyperparameter-tuning, deep-learning and interfaces to other machine-learning software packages. TMVA is additionally integrated with Jupyter, making it accessible with a browser