Inferring potential landscapes from noisy trajectories of particles within an optical feedback trap

J. Shepard Bryan, Prithviraj Basak, John Bechhoefer, Steve Pressé

Research output: Contribution to journalArticlepeer-review

Abstract

While particle trajectories encode information on their governing potentials, potentials can be challenging to robustly extract from trajectories. Measurement errors may corrupt a particle's position, and sparse sampling of the potential limits data in higher energy regions such as barriers. We develop a Bayesian method to infer potentials from trajectories corrupted by Markovian measurement noise without assuming prior functional form on the potentials. As an alternative to Gaussian process priors over potentials, we introduce structured kernel interpolation to the Natural Sciences which allows us to extend our analysis to large datasets. Structured-Kernel-Interpolation Priors for Potential Energy Reconstruction (SKIPPER) is validated on 1D and 2D experimental trajectories for particles in a feedback trap.

Original languageEnglish (US)
Article number104731
JournaliScience
Volume25
Issue number9
DOIs
StatePublished - Sep 16 2022

Keywords

  • Optics
  • Physics
  • Statistical physics

ASJC Scopus subject areas

  • General

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