\documentclass[10pt,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{06.04.2018} \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 $\mu-3e$ 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 face this, a recurrent neural network was used which found the right track for $87 \%$ of these particles. \newpage \tableofcontents \newpage \include{01_Standard_Model} %\include{02_Setup} % %\include{03_Datatable} % %\include{04_Results} % %\include{05_Graphics} % %\include{06_Calculus} % %\include{07_Error-Calculus} %\include{08_Bibliography} \end{document}