\documentclass[12pt,a4paper]{article}
\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{graphicx}
\usepackage{fancyhdr}
\usepackage{paralist}
\pagestyle{fancy}
\usepackage{pdfpages}
\usepackage{subcaption}
\usepackage{float}
\usepackage{hyperref}
\usepackage{physics}
\usepackage{tabstackengine}
\usepackage[bottom]{footmisc}
\setlength{\skip\footins}{1.2pc plus 5pt minus 2pt}
\title{Particle Track reconstruction using a recurrent neural network at the $\mu-3e$ experiment}
\author{\textit{Bachelor thesis of}\\ Sascha Liechti}
\date{08.08.2018}

\lhead{}
\chead{}
\rhead{}

\lfoot{}
\cfoot{\thepage}
\rfoot{}

\renewcommand{\headrulewidth}{0.4pt}
\renewcommand{\footrulewidth}{0pt}

\setlength{\parindent}{0pt}

\begin{document}

\maketitle

\begin{center}
\textit{Supervised by}\\
Prof. Nicola Serra\\
Dr. Patrick Owen\\
\end{center}

\textbf{\large{Abstract}}
During the Mu3e experiment we faced the challenge of reconstructing the paths of certain low momentum particles that curled back into the detector and cause additional hits. To tackle this problem, two recurrent neural networks were used in sequence, which found the right track for $87 \%$ of these particles.

\newpage
\tableofcontents

\newpage

\include{01_Standard_Model}

\include{02_mu_to_3e_decay}

\include{03_experimental_setup}

\include{04_machine_learning}

\include{05_Data}

\include{06_RNN_used}

\include{07_Analysis}

\include{08_Appendix}

\bibliographystyle{unsrt}
\bibliography{bib/General}
\end{document}
