Tag: Maria A. Iati̇̀

Faster and more accurate geometrical-optics optical force calculation using neural networks published in ACS Photonics

Focused rays scattered by an ellipsoidal particles (left). Optical torque along y calculated in the x-y plane using ray scattering with a grid of 1600 rays (up, right) and using a trained neural network (down, right). (Image by the Authors of the manuscript.)
Faster and more accurate geometrical-optics optical force calculation using neural networks
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Gunther Barbosa, Antonio A. R. Neves, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò
ACS Photonics 10, 234–241 (2023)
doi: 10.1021/acsphotonics.2c01565
arXiv: 2209.04032

Optical forces are often calculated by discretizing the trapping light beam into a set of rays and using geometrical optics to compute the exchange of momentum. However, the number of rays sets a trade-off between calculation speed and accuracy. Here, we show that using neural networks permits one to overcome this limitation, obtaining not only faster but also more accurate simulations. We demonstrate this using an optically trapped spherical particle for which we obtain an analytical solution to use as ground truth. Then, we take advantage of the acceleration provided by neural networks to study the dynamics of an ellipsoidal particle in a double trap, which would be computationally impossible otherwise.

Presentation by D. Bronte Ciriza at the 19th Electromagnetic and Light Scattering Conference

Comparison between the Geometrical Optics (GO) method and the Neural Network (NN) for the optical forces and torques calculation. The NN improvement in speed and accuracy could help to study the motion of active particles in optical landscapes. Image by D. Bronte Ciriza.
Machine learning to enhance the calculation of optical forces in the geometrical optics approximation
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò
Submitted to: ELS-XIX (2021)
Date: 14 July
Time: 12:50 CEST

Short Abstract:
We show how machine learning can improve the speed and accuracy of the optical force calculations in the geometrical optics approximation

Extended Abstract:
Since the pioneering work by Ashkin in the 1970’s, optical forces have played a fundamental role in fields like biology, nanotechnology, or atomic physics. In all these fields, numerical simulations are of great help for validating theories, for the planning of experiments, and in the interpretation of the results. However, the calculation of the forces is computationally expensive and prohibitively slow for numerical simulations when the forces need to be calculated many times in a sequential way.

Recently, machine learning has been demonstrated to be a promising approach to improve the speed of these calculations and therefore, to expand the applicability of numerical simulations for experimental design and analysis. In this work we show that machine learning can be used to improve not only the speed but also the accuracy of the force calculation in the geometrical optics regime, valid when the particles are significantly bigger than the wavelength of the incident light. This is first demonstrated for the case of a spherical particle with 3 degrees of freedom and later expanded to 9 degrees of freedom by including all the relevant parameters involved in the optical forces calculation. Machine learning is proved as a compact, accurate, and fast approach for optical forces calculation and presents a tool that can be used to study systems that, due to computation limitations, were out of the scope of the traditional ray optics approach.

Optical tweezers in a dusty universe published in The European Physical Journal Plus

Pictorial representation of space tweezers, space applications of optical tweezers. Interplanetary or planetary dust can be collected and investigated directly in situ (open space or extraterrestrial surfaces). The inset represents a closeup of a grain of interplanetary dust trapped by a single-beam optical tweezers. (Image by Alessandro Magazzù)
Optical tweezers in a dusty universe
P. Polimeno, A. Magazzù, M. A. Iatì, R. Saija, L. Folco, D. Bronte Ciriza, M. G. Donato, A. Foti, P. G. Gucciardi, A. Saidi, C. Cecchi-Pestellini, A. Jimenez Escobar, E. Ammannito, G. Sindoni, I. Bertini, V. Della Corte, L. Inno, A. Ciaravella, A. Rotundi & O. M. Maragò
Eur. Phys. J. Plus 136, 339 (2021)
doi: 10.1140/epjp/s13360-021-01316-z

Abstract:
Optical tweezers are powerful tools based on focused laser beams. They are able to trap, manipulate, and investigate a wide range of microscopic and nanoscopic particles in different media, such as liquids, air, and vacuum. Key applications of this contactless technique have been developed in many fields. Despite this progress, optical trapping applications to planetary exploration are still to be developed. Here we describe how optical tweezers can be used to trap and characterize extraterrestrial particulate matter. In particular, we exploit light scattering theory in the T-matrix formalism to calculate radiation pressure and optical trapping properties of a variety of complex particles of astrophysical interest. Our results open perspectives in the investigation of extraterrestrial particles on our planet, in controlled laboratory experiments, aiming for space tweezers applications: optical tweezers used to trap and characterize dust particles in space or on planetary bodies surface.