从噪声轨迹推断潜在景观
Inferring potential landscapes from noisy trajectories
摘要：
虽然粒子轨迹编码关于其支配势的信息，但是从轨迹中鲁棒地提取势可能具有挑战性。测量误差可能会破坏粒子的位置，而对电势的稀疏采样限制了高能区域（如势垒）的数据。我们开发了一种贝叶斯方法来推断电位的任意形状的测量噪声。作为替代高斯过程先验的潜力，我们引入结构化的内核插值的自然科学，使我们能够扩展我们的分析，以大型数据集。我们的方法进行了验证的1D和2D实验轨迹的粒子在反馈陷阱。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
