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Optimal directed current of a brownian motor under a non-gaussian noise generated by a multiplicative process

机译:乘法过程产生的非高斯噪声下布朗电机的最佳定向电流

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Unidirectional motion is achieved when a particle, moving under the influence of an underlying noise source, is subjected to a ratchet asymmetric periodic potential. Here, we investigate how deviations from the Gaussian nature of the noise distribution function impacts the average particle's current. The input noise is considered to be produced by a Langevin process including both multiplicative and additive random noise sources. The resulting input random signal has a power-law amplitude distribution and a finite correlation time. These features are controlled by the average of the multiplicative noise. We show that the average particle's velocity depends non-monotonically on the degree of non-Gaussianity of the input noise. It exhibits a maximum at an intermediate value of the effective power-law exponent that characterizes the asymptotic decay of the noise probability distribution function.
机译:当粒子在潜在噪声源的影响下运动时,受到棘轮非对称周期性电势的作用,就可以实现单向运动。在这里,我们研究与噪声分布函数的高斯性质的偏差如何影响平均粒子的电流。输入噪声被认为是由包括乘法和加法随机噪声源在内的Langevin过程产生的。所得的输入随机信号具有幂律幅度分布和有限的相关时间。这些特征由乘法噪声的平均值控制。我们证明了平均粒子的速度非单调地取决于输入噪声的非高斯程度。它在有效幂律指数的中间值处表现出最大值,该中间值表征了噪声概率分布函数的渐近衰减。

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