# Motivation and Introduction #
***Welcome to data analysis!*** [^1]
Comparison of measurements to theoretical models is one of the standard
tasks in experimental physics. In the most simple case, a "model" is
just a function providing predictions of measured data. Very often, the
model depends on parameters. Such a model may simply state "the current
*I* is proportional to the voltage *U*", and the task of the
experimentalist consists of determining the resistance, *R*, from a set
of measurements.
As a first step, a visualisation of the data is needed. Next, some
manipulations typically have to be applied, e.g. corrections or
parameter transformations. Quite often, these manipulations are complex
ones, and a powerful library of mathematical functions and procedures
should be provided - think for example of an integral or peak-search or
a Fourier transformation applied to an input spectrum to obtain the
actual measurement described by the model.
One specialty of experimental physics are the inevitable errors
affecting each measurement, and visualisation tools have to include
these. In subsequent analysis, the statistical nature of the errors must
be handled properly.
As the last step, measurements are compared to models, and free model
parameters need to be determined in this process. See Figure [1.1](#f11) for an
example of a function (model) fit to data points. Several standard methods are
available, and a data analysis tool should provide easy access to more
than one of them. Means to quantify the level of agreement between
measurements and model must also be available.
[f11]: figures/examplefit.png "f11"
![Measured data points with error bars and fitted quadratic
function.\label{f11}][f11]
Quite often, the data volume to be analyzed is large - think of
fine-granular measurements accumulated with the aid of computers. A
usable tool therefore must contain easy-to-use and efficient methods for
data handling.
In Quantum mechanics, models typically only predict the probability
density function ("pdf") of measurements depending on a number of
parameters, and the aim of the experimental analysis is to extract the
parameters from the observed distribution of frequencies at which
certain values of the measurement are observed. Measurements of this
kind require means to generate and visualize frequency distributions,
so-called histograms, and stringent statistical treatment to extract the
model parameters from purely statistical distributions.
Simulation of expected data is another important aspect in data
analysis. By repeated generation of "pseudo-data", which are analysed in
the same manner as intended for the real data, analysis procedures can
be validated or compared. In many cases, the distribution of the
measurement errors is not precisely known, and simulation offers the
possibility to test the effects of different assumptions.
## Welcome to ROOT ##
A powerful software framework addressing all of the above requirements
is ROOT [@ROOT], an open source project coordinated by the European
Organisation for Nuclear Research, CERN in Geneva. ROOT is very flexible
and provides both a programming interface to use in own applications and
a graphical user interface for interactive data analysis. The purpose of
this document is to serve as a beginners guide and provides extendable
examples for your own use cases, based on typical problems addressed in
student labs. This guide will hopefully lay the ground for more complex
applications in your future scientific work building on a modern,
state-of the art tool for data analysis.
This guide in form of a tutorial is intended to introduce you to the
ROOT package in about *30* pages. This goal will be accomplished using
concrete examples, according to the "learning by doing" principle. Also
because of this reason, this guide cannot cover the complexity of the
ROOT package. Nevertheless, once you feel confident with the concepts
presented in the following chapters, you will be able to appreciate the
ROOT Users Guide [@ROOT_Users_Guide] and navigate through the Class
Reference [@ROOT_Class_Reference] to find all the details you might be
interested in. You can even look at the code itself, since ROOT is a
free, open-source product. Use these documents in parallel to this
tutorial!
The ROOT Data Analysis Framework itself is written in and heavily relys
on the programming language `C++`, and therefore some knowledge about
`C` and `C++` is required. Eventually, just profit from the immense
available literature about `C++` if you do not have any idea of what
object oriented programming could be.
ROOT is available for many platforms (Linux, Mac OS X, Windows...), but
in this guide we will implicitly assume that you are using Linux. The
first thing you need to do with ROOT is install it, don't you? Obtaining
the latest ROOT version is straightforward. Just seek the "Pro" version
on this webpage .
You will find precompiled versions for the different architectures, or
the ROOT source code to compile yourself. Just pick up the flavour you
need and follow the installation instructions.
**Let's dive into ROOT!**
[^1]: This guide was prepared for the ROOT IRMM Tutorial adapting "A
ROOT Guide for Students"
, a document by D.
Piparo, G. Quast and M. Zeise.