Application of Machine Learning for Energy Reconstruction in the ATLAS Liquid Argon Calorimeter

The beam intensity of the Large Hadron Collider will be significantly increased during the Phase-II long shut down of 2024-2026. Signal processing techniques that are used to extract the energy of detected particles in ATLAS will suffer a significant loss in performance under these conditions. This study compares the presently used optimal filter technique to alternative machine learning algorithms for signal processing. The machine learning algorithms are shown to outperform the optimal filter in many relevant metrics for energy extraction. This thesis also explores the implementation of machine learning algorithms on ATLAS hardware.