论文标题
从嘈杂的轨迹中推断潜在的景观
Inferring potential landscapes from noisy trajectories
论文作者
论文摘要
虽然粒子轨迹编码有关其管理电位的信息，但潜力可能是从轨迹中鲁棒提取的挑战。测量误差可能会破坏粒子的位置，并稀疏对较高能量区域（例如屏障）中的潜在限制数据进行稀疏采样。我们开发了一种贝叶斯方法来推断任意形状的电势以及测量噪声。为了替代高斯工艺先验，我们将结构化的内核插值引入了自然科学，这使我们能够将分析扩展到大型数据集。我们的方法在反馈陷阱中的颗粒上的1D和2D实验轨迹进行了验证。
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 of arbitrary shape alongside measurement noise. 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 data sets. Our method is validated on 1D and 2D experimental trajectories for particles in a feedback trap.
