Presentation of master thesis in physics. The title of the master thesis is "Deep Learning for Optical Tweezers. DeepCalib Implementation for Brownian Motion with Delayed Feedback".
Examiner: Giovanni Volpe
Supervisor: Aykut Argun
Opponent: Ivan Gentile Japiassu
Brownian motion with delayed feedback theoretically studied to take control of Brownian particle movement’s direction. One can use optical tweezers to implement delayed feedback. Calibrating optical tweezers with delay implemented is not an easy job. In this study, Deep learning technique using Long Short Term Memory (LSTM) layer as main composition of the model to calibrate the trap stiffness and to measure the delayed feedback employed, using the trapped particle trajectory as an input. We demonstrate that this approach is outperforming variance methods in order to calibrate stiffness, also outperforming approximation method to measure the delay in harmonic trap case.