Presentation by A. Callegari at SPIE-ETAI, San Diego, 23 August 2022

Simplified sketch of the neural network used for the simulations of intracavity optical trapping. (Image by A. Callegari.)
Simulating intracavity optical trapping with machine learning
Agnese Callegari, Mathias Samuelsson, Antonio Ciarlo, Giuseppe Pesce, David Bronte Ciriza, Alessandro Magazzù, Onofrio M. Maragò, Antonio Sasso, Giovanni Volpe
Submitted to SPIE-ETAI
Date: 23 August 2022
Time: 13:40 (PDT)

Intracavity optical tweezers have been proven successful for trapping microscopic particles at very low average power intensity – much lower than the one in standard optical tweezers. This feature makes them particularly promising for the study of biological samples. The modeling of such systems, though, requires time-consuming numerical simulations that affect its usability and predictive power. With the help of machine learning, we can overcome the numerical bottleneck – the calculation of optical forces, torques, and losses – reproduce the results in the literature and generalize to the case of counterpropagating-beams intracavity optical trapping.

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