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\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}

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

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\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}

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