Thermodynamic inference based on coarse-grained data or noisy measurements.
Thermodynamic inference based on coarse-grained data or noisy measurements.
复制标题
基于粗粒度数据或噪声测量的热力学推断。
作者：
Reinaldo García;S. Lahiri;D. Lacoste
涨落定理已成为单分子生物物理学中测量非平衡态实验自由能差的重要工具。当显著的粗粒化或噪声影响测量时，自由能的确定变得具有挑战性。为了解决这个热力学推理问题，我们提出了改进的估计的自由能差异波动定理的基础上，我们测试的一些例子。当工件呈高斯分布且与工件上的误差不相关时，噪声的影响可以通过有效温度来描述，该有效温度仅取决于信噪比。有效温度的概念似乎不太有用的非高斯工作分布或当误差与工作相关，但尽管如此，正如我们所示，改进的估计仍然可以构建这种情况下。作为一个例子，非平凡的误差和工作之间的相关性，我们还考虑测量延迟，线性朗之万方程所描述的。
Fluctuation theorems have become an important tool in single-molecule biophysics to measure free-energy differences from nonequilibrium experiments. When significant coarse-graining or noise affect the measurements, the determination of the free energies becomes challenging. In order to address this thermodynamic inference problem, we propose improved estimators of free-energy differences based on fluctuation theorems, which we test on a number of examples. The effect of the noise can be described by an effective temperature, which only depends on the signal-to-noise ratio, when the work is Gaussian distributed and uncorrelated with the error made on the work. The notion of effective temperature appears less useful for non-Gaussian work distributions or when the error is correlated with the work, but nevertheless, as we show, improved estimators can still be constructed for such cases. As an example of nontrivial correlations between the error and the work, we also consider measurements with delay, as described by linear Langevin equations.