Tag: J. Miguel Rubi

Cost of Thermal Noise Suppression published in J. Phys. A.

Thermal noise suppression: How much does it cost?

Thermal noise suppression: How much does it cost?
Giovanni Volpe, Jan Wehr, Dmitri Petrov & J. Miguel Rubi
Journal of Physics A: Mathematical and Theoretical 42(9), 095005 (2009)
DOI: 10.1088/1751-8113/42/9/095005
arXiv: 0711.0923

In order to stabilize the behavior of noisy systems, confining it around a desirable state, an effort is required to suppress the intrinsic noise. This noise suppression task entails a cost. For the important case of thermal noise in an overdamped system, we show that the minimum cost is achieved when the system control parameters are held constant: any additional deterministic or random modulation produces an increase of the cost. We discuss the implications of this phenomenon for those overdamped systems whose control parameters are intrinsically noisy, presenting a case study based on the example of a Brownian particle optically trapped in an oscillating potential.

Stochastic Resonant Damping published in Phys. Rev. E

Stochastic resonant damping in a noisy monostable system: Theory and experiment

Stochastic resonant damping in a noisy monostable system: Theory and experiment
Giovanni Volpe, Sandro Perrone, J. Miguel Rubi & Dmitri Petrov
Physical Review E 77(5), 051107 (2008)
DOI: 10.1103/PhysRevE.77.051107

Usually in the presence of a background noise an increased effort put in controlling a system stabilizes its behavior. Rarely it is thought that an increased control of the system can lead to a looser response and, therefore, to a poorer performance. Strikingly there are many systems that show this weird behavior; examples can be drawn form physical, biological, and social systems. Until now no simple and general mechanism underlying such behaviors has been identified. Here we show that such a mechanism, named stochastic resonant damping, can be provided by the interplay between the background noise and the control exerted on the system. We experimentally verify our prediction on a physical model system based on a colloidal particle held in an oscillating optical potential. Our result adds a tool for the study of intrinsically noisy phenomena, joining the many constructive facets of noise identified in the past decades—for example, stochastic resonance, noise-induced activation, and Brownian ratchets.