bachelor_thesis/latex/tex/introduction.tex
2020-06-04 16:13:41 +02:00

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\chapter{Introduction}%
\label{chap:intro}
MC (MC) methods have been and still are one of the most important
tools for numerical calculations in particle physics. Be it for
validating the well established Standard Model or for making
predictions about new theories, MC simulations are the
crucial interface of theory and experimental data, making them
directly comparable.% Furthermore horizontal scaling is almost trivial
% to implement in MC algorithms, making them well adapted to
% modern parallel computing.
In this thesis, the use of MC methods will be traced through from
simple integration to the simulation of proton-proton scattering.
The ``Guinea Pig'' of this thesis is the quark annihilation into two
photons \(\qqgg\), henceforth called the diphoton process. It forms an
important background to the Higgs decay channel
\(H\rightarrow \gamma\gamma\) (which was instrumental in its
discovery) and to a dihiggs decay
\(HH\rightarrow b\bar{b}\gamma\gamma\)~\cite{aaboud2018:sf}, while
still being a pure QED process at leading order and thus calculable by
hand within the scope of this thesis. The differential and total cross
section of this process is being calculated in leading order in
\cref{chap:qqgg} and the obtained result is compared to the total
cross section obtained with the \sherpa~\cite{Gleisberg:2008ta} event
generator, used as matrix element integrator. In \cref{chap:mc} some
simple MC methods are discussed, implemented and their results
compared. Beginning with a study of MC integration the \vegas\
algorithm~\cite{Lepage:19781an} is implemented and
evaluated. Subsequently MC sampling methods are explored and the
output of \vegas\ is used to improve the sampling
efficiency. Histograms of observables are generated and compared to
histograms from \sherpa\ using the \rivet~\cite{Bierlich:2019rhm}
analysis framework. \Cref{chap:pdf} deals with proton-proton
scattering in the partonic picture using parton density functions,
ending with the implementation of a simple event generator for
\(\ppgg\) scattering at \lhc\ conditions. Some integration and
sampling algorithms and their implementation are adapted to the
multidimensional case and histograms of observables are generated with
good efficiency. Because a real \(pp\) scattering event also
incorporates processes like parton showers, hadronization and multiple
interactions, a realistic simulation should account for those
effects. The impact of those effects on observables is studied in
\cref{chap:pheno} using the \sherpa\ event generator.
\section{Conventions}%
\label{sec:convent}
Throughout natural units with \(c=1, \hbar = 1, k_B=1, \varepsilon_0
= 1\) are used unless stated otherwise. The QED coupling constant was
set to the value \(\alpha = 1/137.036\) in \sherpa.
\section{Source Code}%
\label{sec:source}
The (literate) python code, used to generate most of the results and
figures can be found under
\url{https://github.com/vale981/bachelor_thesis/} and more
specifically in the subdirectory \texttt{prog/python/qqgg}.
The file \texttt{monte\_carlo.py} implements all the monte-carlo
algorithm related functionality as a module. The file
\texttt{analytical\_xs.org} contains a literate computation notebook
that generates all the results of \cref{chap:mc}. The file
\texttt{parton\_density\_function\_stuff.org} contains all the
computations for \cref{chap:pdf}. The python code makes heavy use of
\href{https://www.scipy.org/}{scipy}~\cite{2020Virtanen:Sc} (and of
course \href{https://numpy.org/}{numpy}).
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