Charged particle distributions and robustness of the neural network pixel clustering in ATLAS
Friday 09 September 2016
to 15:00 at
Edvin Sidebo (Physics Department)
The thesis contains a study of the robustness of the artificial neural network used in the ATLAS track reconstruction algorithm as a tool to recover tracks in dense environments. Different variations, motivated by potential discrepancies between data and simulation, are performed to the neural network's input while monitoring the corresponding change in the output. Within resaonable magnitudes, the neural networks prove to be robust to most variations.