Real-time calibration of a feedback trap

Area of Science:

• Physics
• Physical Chemistry
• Statistical Mechanics

Background:

• Feedback traps utilize closed-loop control for manipulating particles and molecules in solution.
• Applications in measuring particle properties and non-equilibrium statistical mechanics are limited by electric force drifts over time.

Purpose of the Study:

• To develop a method for real-time measurement and control of electric and stochastic forces in feedback traps over extended durations (hours).
• To overcome limitations imposed by electric force drifts in particle manipulation experiments.

Main Methods:

• Implementation of a recursive maximum likelihood (RML) algorithm for real-time data analysis and feedback control.
• Simulations to validate the RML algorithm's accuracy in parameter recovery.
• Experimental application to measure diffusion coefficients of particles.

Main Results:

• The RML algorithm demonstrated the ability to perform real-time measurement and control of forces over hours.
• Simulations confirmed accurate recovery of known parameters by the RML algorithm.
• Experimental diffusion coefficient estimates aligned with expected physical properties.

Conclusions:

• The RML algorithm significantly enhances the stability and duration of feedback trap experiments.
• This advancement enables more robust measurements and exploration of non-equilibrium systems.
• The RML algorithm provides a powerful tool for precise manipulation and characterization of small systems.