Predicting particle properties in optical traps with machine learning

Lachlan H. W. Hamilton; Lauren R. McQueen; Oscar W. Smee; Timo A. Nieminen; Halina Rubinsztein-Dunlop; Isaac C. D. Lenton

doi:10.1117/12.2581341

17 September 2020 Predicting particle properties in optical traps with machine learning

Lachlan H. W. Hamilton, Lauren R. McQueen, Oscar W. Smee, Timo A. Nieminen, Halina Rubinsztein-Dunlop, Isaac C. D. Lenton

Author Affiliations +

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114691Z (2020) https://doi.org/10.1117/12.2581341
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Abstract

Identifying a particle in an optical trap can be a difficult task, especially for biological samples with low contrast. The relationship of radius and refractive index to the stiffness of optical traps is non-intuitive, motivating a machine learning approach. We demonstrate methods for real-time estimates of the radius and refractive index of particles trapped by optical tweezers. This is achieved by analysing the particle’s position and force with artificial neural networks. Our network achieved binary classification of experimental particles by sampling only milliseconds of force and position values. This demonstrates that real-time particle recognition is achievable with machine learning systems.

Conference Presentation

Citation Download Citation

Lachlan H. W. Hamilton, Lauren R. McQueen, Oscar W. Smee, Timo A. Nieminen, Halina Rubinsztein-Dunlop, and Isaac C. D. Lenton "Predicting particle properties in optical traps with machine learning", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691Z (17 September 2020); https://doi.org/10.1117/12.2581341

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available