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ROOT

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From ROOT's webpage

A modular scientific software toolkit. It provides all the functionalities needed to deal with big data processing, statistical analysis, visualisation and storage. It is mainly written in C++ but integrated with other languages such as Python and R.

It is the primary toolkit for many experimental analysis and while you are free to analyze these datasets however you like, some familiarity with ROOT will serve you well when accessing the data.

To get started analyzing data with ROOT and C++, start with C++ and ROOT. A ROOT installation comes with the virtual machine and/or docker installation pointed to in this guide.

To learn more about ROOT, see the ROOT Manual.

  • Many ROOT examples can be found here. If you don't know where to start, we would recommend

    • fillrandom.C - fill in a 1D histogram from a parametric function
    • basic.C - read in data and create a root file
    • h1ReadAndDraw.c - read in a 1D histogram from a ROOT file, and then draw the histogram
    • draw2dopt.C - explore 2D drawing options

  • Python has become the language of choice for many analysts and most of the examples you'll see make use of the PyROOT module, callable from python. For more on pyROOT, see Python interface: PyROOT. You can go through a number of examples here. If you don't know where to start, we would recommend

    • hsimple.py - create and draw histograms
    • fillrandom.py - fill in a 1D histogram from a parametric function, and save your output as a root file
    • fit1.py - open the root file created from fillrandom.py, and do a fit