标题： 主动布朗粒子壁面力矩的贝叶斯推断 显示英文标题

标题： Bayesian inference of wall torques for active Brownian particles

摘要： 生物体的运动和合成自推进物体的运动通常使用活性布朗粒子来描述。 为了捕捉这些粒子与其往往复杂的环境之间的相互作用，该模型可以增加经验力或力矩，例如，以描述它们在与障碍物或墙壁碰撞后对齐的情况。 在此，我们通过将这些经验模型的输出预测与棒状活性粒子在平坦墙壁上发生立体散射的轨迹进行比较，来评估这些模型的质量。 我们采用经典的最小二乘法来评估瞬时力矩。 此外，我们提出了一种贝叶斯推断方法来构建合理模型参数的后验分布。 与最小二乘拟合不同，贝叶斯方法不需要活性粒子的方向数据，并可直接应用于实验跟踪数据。 显示英文摘要

摘要： The motility of living things and synthetic self-propelled objects is often described using Active Brownian particles. To capture the interaction of these particles with their often complex environment, this model can be augmented with empirical forces or torques, for example, to describe their alignment with an obstacle or wall after a collision. Here, we assess the quality of these empirical models by comparing their output predictions with trajectories of rod-shaped active particles that scatter sterically at a flat wall. We employ a classical least-squares method to evaluate the instantaneous torque. In addition, we lay out a Bayesian inference procedure to construct the posterior distribution of plausible model parameters. In contrast to the least squares fit, the Bayesian approach does not require orientational data of the active particle and can readily be applied to experimental tracking data.