ROOT is an object-oriented framework aimed at solving the data analysis challenges of high-energy physics. Here we discuss the main components of the framework. We begin with an overview describing the framework's organization, the interpreter CINT, its automatic interface to the compiler and linker ACLiC, and an example of a first interactive session. The subsequent sections cover histogramming and fitting. Then, ROOT's solution to storing and retrieving HEP data, building and managing of ROOT files, and designing ROOT trees. Followed by a description of the collection classes, the GUI classes, how to add your own classes to ROOT, and PROOF, ROOT's parallel processing facility.

ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, the RooFit package allows the user to perform complex data modeling and fitting while the RooStats library provides abstractions and implementations for advanced statistical tools. Multivariate classification methods based on machine learning techniques are available via the TMVA package. A central piece in these analysis tools are the histogram classes which provide binning of one-and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using onthe-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks -e.g. data mining in HEP -by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way. Antcheva et al. / Computer Physics Communications 180 (2009) [2499][2500][2501][2502][2503][2504][2505][2506][2507][2508][2509][2510][2511][2512] Program summary

ALICE is a general-purpose heavy-ion experiment designed to study the physics of strongly interacting matter and the quark–gluon plasma in nucleus–nucleus collisions at the LHC. It currently involves more than 900 physicists and senior engineers, from both the nuclear and high-energy physics sectors, from over 90 institutions in about 30 countries.The ALICE detector is designed to cope with the highest particle multiplicities above those anticipated for Pb–Pb collisions (dNch/dy up to 8000) and it will be operational at the start-up of the LHC. In addition to heavy systems, the ALICE Collaboration will study collisions of lower-mass ions, which are a means of varying the energy density, and protons (both pp and pA), which primarily provide reference data for the nucleus–nucleus collisions. In addition, the pp data will allow for a number of genuine pp physics studies.The detailed design of the different detector systems has been laid down in a number of Technical Design Reports issued between mid-1998 and the end of 2004. The experiment is currently under construction and will be ready for data taking with both proton and heavy-ion beams at the start-up of the LHC.Since the comprehensive information on detector and physics performance was last published in the ALICE Technical Proposal in 1996, the detector, as well as simulation, reconstruction and analysis software have undergone significant development. The Physics Performance Report (PPR) provides an updated and comprehensive summary of the performance of the various ALICE subsystems, including updates to the Technical Design Reports, as appropriate.The PPR is divided into two volumes. Volume I, published in 2004 (CERN/LHCC 2003-049, ALICE Collaboration 2004 J. Phys. G: Nucl. Part. Phys. 30 1517–1763), contains in four chapters a short theoretical overview and an extensive reference list concerning the physics topics of interest to ALICE, the experimental conditions at the LHC, a short summary and update of the subsystem designs, and a description of the offline framework and Monte Carlo event generators.The present volume, Volume II, contains the majority of the information relevant to the physics performance in proton–proton, proton–nucleus, and nucleus–nucleus collisions. Following an introductory overview, Chapter 5 describes the combined detector performance and the event reconstruction procedures, based on detailed simulations of the individual subsystems. Chapter 6 describes the analysis and physics reach for a representative sample of physics observables, from global event characteristics to hard processes.

The ROOT system started in 1995, at a time when future software directions were unclear. Many ideas were around, many prototypes were developed and many languages were candidate to replace Fortran77, but there were many committees too. Initially started as a successor of the PAW system, ROOT has considerably evolved since its initial conception. Following the long saga with Object-Oriented databases, more emphasis has been put on data modelling, data storage and data access. This paper describes the main features of ROOT as it is today and discusses the many steps, controversies and challenges that had we had to overcome to arrive at the current situation. This long process reflects the fact that systems in High Energy Physics, designed to operate for decades, require a very close collaboration with experiments.

Motivation Agent-based modeling is an indispensable tool for studying complex biological systems. However, existing simulation platforms do not always take full advantage of modern hardware and often have a field-specific software design. Results We present a novel simulation platform called BioDynaMo that alleviates both of these problems. BioDynaMo features a modular and high-performance simulation engine. We demonstrate that BioDynaMo can be used to simulate use cases in: neuroscience, oncology, and epidemiology. For each use case we validate our findings with experimental data or an analytical solution. Our performance results show that BioDynaMo performs up to three orders of magnitude faster than the state-of-the-art baselines. This improvement makes it feasible to simulate each use case with one billion agents on a single server, showcasing the potential BioDynaMo has for computational biology research. Availability BioDynaMo is an open-source project under the Apache 2.0 license and is available at www.biodynamo.org. Instructions to reproduce the results are available in supplementary information. Supplementary information Available at https://doi.org/10.5281/zenodo.5121618.