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https://github.com/vale981/bachelor_thesis
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add some monte carlo theory
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parent
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9 changed files with 217 additions and 34 deletions
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@ -3,7 +3,7 @@ twoside=false,toc=listof,toc=bibliography,fleqn,leqno,
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captions=nooneline,captions=tableabove,english]{scrbook}
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\usepackage{hirostyle}
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%\addbibresource{thesis.bib}
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\addbibresource{thesis.bib}
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\title{Title}
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\author{Valentin Boettcher}
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@ -16,15 +16,15 @@ captions=nooneline,captions=tableabove,english]{scrbook}
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\input{./tex/qqgammagamma/calculation.tex}
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\input{./tex/qqgammagamma/comparison.tex}
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\listoffigures
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\listoftables
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\input{./tex/monte-carlo.tex}
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\input{./tex/monte-carlo/theory.tex}
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\appendix
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\input{./tex/appendix.tex}
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% Bibliography:
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\clearpage
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% \input{./tex/mybibliography.tex}
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\listoffigures
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\listoftables
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\printbibliography
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\end{document}
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@ -10,7 +10,7 @@
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\usepackage{graphicx, booktabs, float, scrhack}
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\usepackage{amsmath,amssymb}
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\usepackage[automark]{scrlayer-scrpage}
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%\usepackage[backend=biber,style=verbose,sortcase=false,language=english]{biblatex}
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\usepackage[backend=biber, language=english, style=phys]{biblatex}
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\usepackage{siunitx}
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\usepackage[pdfencoding=auto]{hyperref} % load late
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% \usepackage[activate={true,nocompatibility},final,tracking=true,spacing=true,factor=1100,stretch=10,shrink=10]{microtype}
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@ -57,9 +57,10 @@ labelformat=brace, position=top]{subcaption}
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\captionsetup{justification=centering}
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%% Labels
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\labelformat{section}{section #1}
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\labelformat{figure}{figure #1}
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\labelformat{table}{table #1}
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\labelformat{chapter}{chapter~#1}
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\labelformat{section}{section~#1}
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\labelformat{figure}{figure~#1}
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\labelformat{table}{table~#1}
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% Macros
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@ -93,3 +94,13 @@ labelformat=brace, position=top]{subcaption}
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%% Notes on Equations
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\newcommand{\shorteqnote}[1]{ & & \text{\small\llap{#1}}}
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%% Sherpa
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\newcommand{\sherpa}{\texttt{Sherpa}}
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%% Expected Value and Variance
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\newcommand{\EX}[1]{\operatorname{E}\qty[#1]}
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\newcommand{\VAR}[1]{\operatorname{VAR}\qty[#1]}
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%% Uppercase Rho
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\newcommand{\Rho}{P}
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@ -7,4 +7,4 @@ thesis: document.tex
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.PHONY: clean
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clean:
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rm -f $(OUTDIR)/*
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rm -rf $(OUTDIR)/*
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12
latex/tex/monte-carlo.tex
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12
latex/tex/monte-carlo.tex
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@ -0,0 +1,12 @@
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%%% Local Variables: ***
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%%% mode: latex ***
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%%% TeX-master: "../document.tex" ***
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%%% End: ***
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\chapter{Survey of Elementary Monte-Carlo Methods}%
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\label{chap:mc}
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As monte-carlo methods for multidimensional integration and sampling
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of probability distributions are central tools of modern particle
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physics, their basics shall be studied. Some simple algorithms will
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be implemented and applied to the results from~\ref{chap:qqgg}.
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latex/tex/monte-carlo/theory.tex
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99
latex/tex/monte-carlo/theory.tex
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@ -0,0 +1,99 @@
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%%% Local Variables: ***
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%%% mode: latex ***
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%%% TeX-master: "../../document.tex" ***
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%%% End: ***
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\section{Monte-Carlo Integration}
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\label{sec:mctheory}
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Consider a function
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\(f: \vb{x}\in\Omega\subset\mathbb{R}^n\mapsto\mathbb{R}\) and a
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probability density on \(\Omega\)
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\(\rho: \vb{x}\in\mathbb{R}^n\mapsto\mathbb{R}_{\geq 0}\) with
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\(\int_{\Omega}\rho(\vb{x})\dd{\vb{x}} = 1\). By multiplying \(f\)
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with a \(1\) in the fashion of~\eqref{eq:baseintegral}, the Integral
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of \(f\) over \(\Omega\) can be interpreted as the expected value
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\(\EX{F/\Rho}\) of the random variable \(F/\Rho\)
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under the distribution \(\rho\).
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\begin{equation}
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\label{eq:baseintegral}
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I = \int_\Omega f(\vb{x}) \dd{\vb{x}} = \int_\Omega
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\qty[\frac{f(\vb{x})}{\rho(\vb{x})}] \rho(\vb{x}) \dd{\vb{x}} = \EX{\frac{F}{\Rho}}
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\end{equation}
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The expected value \(\EX{F/\Rho}\) can be approximate by taking the
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mean of \(F/\Rho\) with \(N\) finite samples
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\(\{\vb{x}_i\}_{i\in\overline{1,N}}\sim\rho\) (distributed according to
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\(\rho\)), where \(N\) is usually a very large integer.
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\begin{equation}
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\label{eq:approxexp}
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\EX{\frac{F}{\Rho}} \approx
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\frac{1}{N}\sum_{i=1}^N\frac{f(\vb{x_i})}{\rho(\vb{x_i})}
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\xrightarrow{N\rightarrow\infty} I
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\end{equation}
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The convergence of~\eqref{eq:approxexp} is due to the nature of the
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expected value~\eqref{eq:evalue-mean} and
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variance~\eqref{eq:variance-mean} of the mean
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\(\overline{X} = \frac{1}{N}\sum_i X_i\) of \(N\) uncorrelated random
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variables \(\{X_i\}_{i\in\overline{1,N}}\) with the same distribution,
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expected value \(\EX{X_i}=\mathbb{E}\) and variance
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\(\sigma_i^2 = \sigma^2\).
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\begin{align}
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\EX{\overline{X}} = \frac{1}{N}\sum_i\EX{X_i} = \mathbb{E} \label{eq:evalue-mean}\\
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\sigma^2_{\overline{X}} = \sum_i\frac{\sigma_i^2}{N^2} =
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\frac{\sigma^2}{N} \label{eq:variance-mean}
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\end{algin}
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Evidently \(\frac{\sigma^2}{N}\xrightarrow{N\rightarrow\infty} 0\)
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thus the~\eqref{eq:approxexp} really converges to \(I\). For finite
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\(N\) value of~\eqref{eq:approxexp} varies around \(I\) with the
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variance \(\VAR{F/\Rho}\cdot N^{-1}\) as in~\eqref{eq:varI}.
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\begin{align}
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\VAR{\frac{F}{\Rho}} &= \int_\Omega \qty[I -
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\frac{f(\vb{x})}{\rho(\vb{x})}]^2 \rho({\vb{x}}) \dd{\vb{x}} =
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\int_\Omega \qty[\qty(\frac{f(\vb{x})}{\rho(\vb{x})})^2 -
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I^2]\rho({\vb{x}}) \dd{\vb{x}} \label{eq:varI}
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\\
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&\approx \frac{1}{N - 1}\sum_i \qty[I -
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\frac{f(\vb{x_i})}{\rho(\vb{x_i})}]^2 \label{eq:varI-approx}
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\end{align}
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The name of the game is thus to reduce \(\VAR{F/\Rho}\) to speed up
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the convergence of~\eqref{eq:approxexp} and achieve higher accuracy
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with fewer function evaluations.
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The simplest choice for \(\rho\) is clearly given
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by~\eqref{eq:simplep}, the uniform distribution.
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\begin{equation}
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\label{eq:simplep}
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\rho(\vb{x}) = \frac{1}{\int_{\Omega}1\dd{\vb{x'}}} =
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\frac{1}{\abs{\Omega}}
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\end{equation}
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With this distribution~\eqref{eq:approxexp}
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becomes~\eqref{eq:approxexp-uniform}. In other words, \(I\) is just
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the mean of \(f\) in \(\Omega\), henceforth
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called \(\bar{f}\), multiplied with the volume.
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\begin{equation}
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\label{eq:approxexp-uniform}
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\EX{\frac{F}{\Rho}} \approx
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\frac{\abs{\Omega}}{N}\sum_{i=1}^N f(\vb{x_i}) = \abs{\Omega}\cdot\bar{f}
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\end{equation}
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The variance \(\VAR{I}=\VAR{F/\Rho}\) is now given
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by~\ref{eq:approxvar-I}. Note that the factor \(\abs{\omega}\) gets
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squared when approximating the integral to the sum.
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\begin{equation}
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\label{eq:approxvar-I}
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\VAR{I} = \abs{\Omega}\int_\Omega f(\vb{x})^2 -
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I^2 \dd{\vb{x}} \equiv \abs{\Omega}\cdot\sigma_f^2 \approx
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\frac{\abs{\Omega}^2}{N-1}\sum_{i}\qty[f(\vb{x}_i) - \bar{f}]^2
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\end{equation}
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@ -1,6 +1,11 @@
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%%% Local Variables: ***
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%%% mode:latex ***
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%%% TeX-master: "../document.tex" ***
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%%% End: ***
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\chapter{Quark-Antiquark Annihilation into
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two Photons}%
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\label{sec:qqgg}
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\label{chap:qqgg}
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Consider the scattering reaction \(\qqgg\). The first order expansion
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of this process is being described by the Feynman diagrams
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@ -1,3 +1,8 @@
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%%% Local Variables: ***
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%%% mode:latex ***
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%%% TeX-master: "../../document.tex" ***
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%%% End: ***
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\section{Calulation of the Cross Section to first Order}%
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\label{sec:qqggcalc}
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@ -1,23 +1,30 @@
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\section{Discussion and Comparison with Sherpa}%
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%%% Local Variables: ***
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%%% mode:latex ***
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%%% TeX-master: "../../document.tex" ***
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%%% End: ***
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\section{Discussion and Comparison with \sherpa}%
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\label{sec:compsher}
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The result obtained in~\ref{sec:qqggcalc} shall now be verified by
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monte-carlo in \verb|Sherpa|. To facilitate this, an expression for
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the total cross section for a range of \(\theta\) or \(\eta\) has to
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be obtained. Using the golden rule for \(2\rightarrow 2\) processes
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and observing that the initial and final momenta are equal
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(\(p_i=p_f\)) and \(g=\sqrt{4\pi\alpha}\), the
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result~\eqref{eq:crossec} arises.
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monte-carlo in \sherpa{}~\cite{Gleisberg:2008ta}. To facilitate this, an
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expression for the total cross section for a range of \(\theta\) or
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\(\eta\) has to be obtained. Using the golden rule for
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\(2\rightarrow 2\) processes and observing that the initial and final
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momenta are equal (\(p_i=p_f\)) and \(g=\sqrt{4\pi\alpha}\), the
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result~\eqref{eq:crossec} arises. The differential cross section is
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also given in terms of the pseudo-rapidity in~\ref{eq:xs-eta}.
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An additional factor of \(\frac{1}{2}\) comes in due to there being
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two identical photons in the final state.
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\begin{equation}
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\label{eq:crossec}
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\dv{\sigma}{\Omega} =
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\frac{1}{2}\frac{1}{(8\pi)^2}\cdot\frac{\abs{\mathcal{M}}^2}{\ecm^2}\cdot\frac{\abs{p_f}}{\abs{p_i}}
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= \overbrace{\frac{\alpha^2Q^4}{6\ecm^2}}^{\mathfrak{C}}\frac{1+\cos^2(\theta)}{\sin^2(\theta)}
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\end{equation}
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\begin{align}
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\dv{\sigma}{\Omega} &=
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\frac{1}{2}\frac{1}{(8\pi)^2}\cdot\frac{\abs{\mathcal{M}}^2}{\ecm^2}\cdot\frac{\abs{p_f}}{\abs{p_i}}
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=
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\underbrace{\frac{\alpha^2Q^4}{6\ecm^2}}_{\mathfrak{C}}\frac{1+\cos^2(\theta)}{\sin^2(\theta)}\label{eq:crossec}
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\\
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\dv{\sigma}{\eta} &= \frac{\alpha^2Q^4}{6\ecm^2}\cdot\qty(\tanh(\eta)^2 + 1)\label{eq:xs-eta}
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\end{align}
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\begin{figure}[ht]
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\centering
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@ -43,12 +50,16 @@ also~\ref{fig:diffxs}) is divergent for angles near zero or
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divergence. Because \(m=0\) is the limit for
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\(\ecm\rightarrow\infty\), the cross section would still have strong
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peaks for angles near \(0,\pi\) at high energies so that the result is
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not altogether nonphysical. It is clearly symmetric around
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not altogether nonphysical. The divergence of the cross section itself
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is also not, the problem here, because it can be transformed into a
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form where the divergence does not occur (see~\eqref{eq:xs-eta}).
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The differential cross section is clearly symmetric around
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\(\theta=\frac{pi}{2}\) as was to be expected, because the photons are
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indistinguishable. To compare the cross section to experiment and to
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simulation an interval around \(\theta=\frac{\pi}{2}\) has to be
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chosen, where the first order, mass-less approximation may yield
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sensible results.
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chosen, where the first order, mass-less approximation may yield a
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useful result.
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The total cross section in such an interval, given by
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integrating~\eqref{eq:crossec} for \(\theta\in [\theta_1, \theta_2]\)
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@ -73,10 +84,12 @@ an interval of \([-\eta, \eta]\), is dominated by the linear
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contributions in~\ref{eq:total-crossec} and would result in an
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infinity if no cut on \(\eta\) would be made. Choosing
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\(\eta\in [-2.5,2.5]\) and \(\ecm=\SI{100}{\giga\electronvolt}\) the
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process was monte carlo integrated in sherpa using the runcard
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process was monte carlo integrated in \sherpa\ using the runcard
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in~\ref{sec:qqggruncard}. This runcard describes the exact same (first
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order) process as the calculated cross section.
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Sherpa yields \result{xs/sherpa_xs}. Plugging the same parameters
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into~\eqref{eq:total-crossec} gives \result{xs/xs} which is
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within the uncertainty range of the Sherpa value.
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The monte carlo integration in \sherpa\ yields
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\result{xs/sherpa_xs}. Plugging the same parameters
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into~\eqref{eq:total-crossec} gives \result{xs/xs} which is within the
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uncertainty range of the \sherpa\ value. This verifies the result for
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the total cross section.
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@ -0,0 +1,38 @@
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@article{Gleisberg:2008ta,
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author = "Gleisberg, T. and Hoeche, Stefan. and Krauss, F. and Schonherr, M. and Schumann, S. and Siegert, F. and Winter, J.",
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archivePrefix = "arXiv",
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doi = "10.1088/1126-6708/2009/02/007",
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eprint = "0811.4622",
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journal = "JHEP",
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pages = "007",
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primaryClass = "hep-ph",
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reportNumber = "FERMILAB-PUB-08-477-T, SLAC-PUB-13420, ZU-TH-17-08, DCPT-08-138, IPPP-08-69, EDINBURGH-2008-30, MCNET-08-14",
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title = "{Event generation with SHERPA 1.1}",
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volume = "02",
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year = "2009"
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}
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@article{Krauss:2001iv,
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author = "Krauss, F. and Kuhn, R. and Soff, G.",
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archivePrefix = "arXiv",
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doi = "10.1088/1126-6708/2002/02/044",
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eprint = "hep-ph/0109036",
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journal = "JHEP",
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pages = "044",
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reportNumber = "CAVENDISH-HEP-01-11",
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title = "{AMEGIC++ 1.0: A Matrix element generator in C++}",
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volume = "02",
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year = "2002"
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}
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@article{Gleisberg:2008fv,
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author = "Gleisberg, Tanju and Hoeche, Stefan",
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archivePrefix = "arXiv",
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doi = "10.1088/1126-6708/2008/12/039",
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eprint = "0808.3674",
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journal = "JHEP",
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pages = "039",
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primaryClass = "hep-ph",
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reportNumber = "SLAC-PUB-13232, IPPP-08-31, DCPT-08-62, MCNET-08-08",
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title = "{Comix, a new matrix element generator}",
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volume = "12",
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year = "2008"
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}
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