Efficient analysis with ROOT: Glossary

You’ll learn how to install ROOT on your system or get access to systems with ROOT pre-installed!

You’ll learn how to use ROOT in C++ and Python!

You’ll learn about commonly used features in ROOT!

You’ll learn how to get help with ROOT!

You’ll learn to do efficient data analysis with an example based on NanoAOD files!

ROOT is accessible in several different ways

Detailed up-to-date instructions can be found at https://root.cern/install

The choice of interactive C++, compiled C++ or Python is based on the use case!

Usage of C++ code, compiled with optimization flags, may save you hours of computing time!

PyROOT lets you use C++ from Python but offers many more advanced features to speed up your analysis in Python. Details about the dynamic Python bindings provided by PyROOT can be found on https://root.cern/manual/python.

ROOT provides many features from histogramming, fitting and plotting to investigating data interactively in C++ and Python

RDataFrame is the recommended entry point for efficient analysis

RDataFrame is lazy: declare first what you want to do and let ROOT run all of your tasks as efficiently as possible in one go, in parallel!

Parallelization on multiple threads requires only the ROOT.EnableImplicitMT() statement

User support is an integral part of ROOT!

https://root.cern is the entry point to find all documentation

The reference guide provides in-depth technical documentation, but also additional explanation for classes and a huge amount of tutorials explaining features with code

The ROOT forum is actively maintained by the ROOT team to support you!

Analysis studies Higgs boson decays to two tau leptons with a muon and a hadronic tau in the final state

The input files are (reduced) CMS NanoAOD, being very close to actual analysis in CMS

The following steps will show in a hands-on the use of RDataFrame in an actual analysis

We reduced the initial datasets by filtering suitable events and selecting interesting observables.

This step includes finding the interesting muon-tau pair in each selected event.

To perform this computationally expensive part of the analysis as efficiently as possible, we enable ROOT’s implicit multi-threading and use RDataFrame in C++!

ROOT::RVec is an extended std::vector, which provides features to deal easily with collections similar to NumPy arrays in Python.

We produce histograms of all physics processes and all observables.

All histograms are produced in a signal region with opposite-signed muon-tau pairs and in a control region with same-signed pairs for the data-driven QCD estimate

This step shows the usage of RDataFrame in Python producing a large number of histograms in a single event loop and in parallel!

The plotting combines all histograms to produce estimates of the physical processes and create a figure with a physical meaning.

The plots show the share of the contributing physical processes to the data, but without systematic uncertainties.

The script shows how you can produce paper quality plots with ROOT!