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4 October 2022 Simulating intracavity optical trapping with machine learning (Conference Presentation)
Agnese Callegari, Antonio Ciarlo, Giuseppe Pesce, Antonio Sasso, David Bronte Ciriza, Onofrio M. Maragò, Alessandro Magazzù, Giovanni Volpe, Mathias Samuelsson
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Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC122040I (2022) https://doi.org/10.1117/12.2633072
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States
Abstract
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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Agnese Callegari, Antonio Ciarlo, Giuseppe Pesce, Antonio Sasso, David Bronte Ciriza, Onofrio M. Maragò, Alessandro Magazzù, Giovanni Volpe, and Mathias Samuelsson "Simulating intracavity optical trapping with machine learning (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC122040I (4 October 2022); https://doi.org/10.1117/12.2633072
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KEYWORDS
Optical tweezers
Machine learning
Optical simulations
Particles
Geometrical optics
Motion models
Modulation
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