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布朗轨迹单次数据点扩散常数的最优拟合。

Optimal fits of diffusion constants from single-time data points of Brownian trajectories.

作者信息

Boyer Denis, Dean David S, Mejía-Monasterio Carlos, Oshanin Gleb

机构信息

Instituto de Física, Universidad Nacional Autónoma de México, DF 04510, México.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2012 Dec;86(6 Pt 1):060101. doi: 10.1103/PhysRevE.86.060101. Epub 2012 Dec 20.

Abstract

Experimental methods based on single particle tracking (SPT) are being increasingly employed in the physical and biological sciences, where nanoscale objects are visualized with high temporal and spatial resolution. SPT can probe interactions between a particle and its environment but the price to be paid is the absence of ensemble averaging and a consequent lack of statistics. Here we address the benchmark question of how to accurately extract the diffusion constant of one single Brownian trajectory. We analyze a class of estimators based on weighted functionals of the square displacement. For a certain choice of the weight function these functionals provide the true ensemble averaged diffusion coefficient, with a precision that increases with the trajectory resolution.

摘要

基于单粒子追踪(SPT)的实验方法在物理和生物科学中越来越多地被采用,在这些领域中,纳米级物体能够以高时间和空间分辨率被可视化。SPT可以探测粒子与其环境之间的相互作用,但要付出的代价是缺乏系综平均,因此缺乏统计数据。在这里,我们解决一个基准问题,即如何准确提取单个布朗轨迹的扩散常数。我们分析了一类基于平方位移加权泛函的估计器。对于权重函数的特定选择,这些泛函提供了真正的系综平均扩散系数,其精度随着轨迹分辨率的提高而增加。

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