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Hidden Markov modeling of single-particle diffusion with stochastic tethering
Amit Federbush1,2, Amit Moscovich3, Yohai Bar-Sinai1,2
1Department of Condensed Matter Physics, Tel Aviv University, Tel Aviv 69978, Israel.
Physical Review. E
|April 18, 2024
Summary
We developed a new algorithm to analyze particle movement in 2D Brownian motion with transient surface tethering. This method accurately infers hidden states and estimates physical parameters from observed trajectories.
Area of Science:
- Physics
- Physical Chemistry
- Statistical Mechanics
Background:
- Particle diffusion statistics are used to study environmental interactions.
- Inferring physical properties from observed particle trajectories is complex.
- Transient tethering to a surface affects particle motion.
Purpose of the Study:
- To analyze 2D Brownian motion with transient surface tethering.
- To develop a method for inferring hidden states and physical parameters.
- To provide a robust algorithm for analyzing complex particle dynamics.
Main Methods:
- Modeling the system as a hidden Markov model (HMM).
- Utilizing an alternating maximization algorithm for inference.
- Employing a saddle-point-like approximation for state and parameter estimation.
Main Results:
- The algorithm reliably infers hidden states and estimates physical parameters.
- The method is insensitive to the initial parameter guess.
- Performance is analyzed across different physical parameter regimes.
Conclusions:
- The developed algorithm accurately analyzes particle dynamics with transient tethering.
- This approach offers a reliable method for extracting physical insights from complex trajectories.
- A free software implementation is available for broader use.
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