Summary
We consider a random dynamical system describing the diffusion of a small-noise Brownian particle in a double-well potential with a periodic perturbation of very large period. According to the physics literature, the system is in stochastic resonance if its random trajectories are tuned in an optimal way to the deterministic periodic forcing. The quality of periodic tuning is measured mostly by the amplitudes of the spectral components of the random trajectories corresponding to the forcing frequency. Reduction of the diffusion dynamics in the small noise limit to a Markov chain jumping between its meta-stable states plays an important role.
We study two different measures of tuning quality for stochastic resonance, with special emphasis on their robustness properties when passing to the reduced dynamics of the Markov chains in the small noise limit. The first one is the physicists favourite, spectral power amplification. It is analyzed by means of the spectral properties of the diffusion’s infinitesimal generator in a framework where the system switches every half period between two spatially antisymmetric potential states. Surprisingly, resonance properties of diffusion and Markov chain differ due to the crucial significance of small intra-well fluctuations for spectral concepts. To avoid this defect, we design a second measure of tuning quality which is based on the pure transition mechanism between the meta-stable states. It is investigated by refined large deviation methods in the more general framework of smooth periodically varying potentials, and proves to be robust for the passage to the reduced dynamics.
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References
V. S. Anishchenko, A. B. Neiman, F. Moss, and L. Schimansky-Geier. Stochastic resonance: noise-enhanced order. Physics-Uspekhi, 42(1):7–36, 1999.
A. Bovier, M. Eckhoff, V. Gayrard, and M. Klein. Metastability in reversible diffusion processes I. Sharp asymptotics for capacities and exit times. Preprint No. 767, Weierstraß-Institut für angewandte Analysis und Stochastik (WIAS), Berlin, 2002. To appear in J. Eur. Math. Soc.
N. Berglund and B. Gentz. A sample-paths approach to noise-induced synchronization: Stochastic resonance in a double-well potential. Ann. Appl. Probab., 12(4):1419–1470, 2002.
N. Berglund and B. Gentz. Beyond the Fokker-Planck equation: Pathwise control of noisy bistable systems. J. Phys. A, Math. Gen., 35(9):2057–2091, 2002.
N. Berglund and B. Gentz. Pathwise description of dynamic pitchfork bifurcations with additive noise. Probab. Theory Relat. Fields, 122(3):341–388, 2002.
A. Bovier, V. Gayrard, and M. Klein. Metastability in reversible diffusion processes II. Precise asymptotics for small eigenvalues. Preprint No. 768, Weierstraß-Institut für angewandte Analysis und Stochastik (WIAS), Berlin, 2002. To appear in J. Eur. Math. Soc.
R. Benzi, G. Parisi, A. Sutera, and A. Vulpiani. The mechanism of stochastic resonance. J. Phys. A, 14:453–457, 1981.
R. Benzi, G. Parisi, A. Sutera, and A. Vulpiani. Stochastic resonance in climatic changes. Tellus, 34:10–16, 1982.
R. Benzi, G. Parisi, A. Sutera, and A. Vulpiani. A theory of stochastic resonance in climatic change. SIAM J. Appl. Math., 43:563–578, 1983.
M. I. Budyko. The effect of solar radiation variations on the climate of the earth. Tellus, 21:611–619, 1969.
[DLM+95]_M. I. Dykman, D. G. Luchinskii, R. Mannella, P. V. E. McClintock, N. D. Stein, and N. G. Stocks. Stochastic resonance in perspective. Nuovo Cimento D, 17:661–683, 1995.
A. Erdélyi. Asymptotic expansions. Dover Publications, Inc., New York, 1956.
J.-P. Eckmann and L. E. Thomas. Remarks on stochastic resonance. J. Phys. A, 15:261–266, 1982.
M. I. Freidlin. Quasi-deterministic approximation, metastability and stochastic resonance. Physica D, 137(3–4):333–352, 2000.
L. Gammaitoni, P. Hänggi, P. Jung, and F. Marchesoni. Stochastic resonance. Reviews of Modern Physics, 70:223–287, January 1998.
S. Herrmann and P. Imkeller. The exit problem for diffusions with time periodic drift and stochastic resonance. To appear in Ann. Appl. Probab.
S. Herrmann and P. Imkeller. Barrier crossings characterize stochastic resonance. Stochastics and Dynamics, 2(3):413–436, 2002.
P. Imkeller. Energy balance models — viewed from stochastic dynamics. In Imkeller, P. et al., editors, Stochastic climate models. Proceedings of a workshop, Chorin, Germany, Summer 1999., volume 49 of Prog. Probab., pages 213–240, Basel, 2001. Birkhäuser.
P. Imkeller and I. Pavlyukevich. Stochastic resonance in two-state Markov chains. Arch. Math., 77(1):107–115, 2001.
P. Imkeller and I. Pavlyukevich. Model reduction and stochastic resonance. Stochastics and Dynamics, 2(4):463–506, 2002.
H. A. Kramers. Brownian motion in a field of force and the diffusion model of chemical reactions. Physica, 7:284–304, 1940.
B. McNamara and K. Wiesenfeld. Theory of stochastic resonance. Physical Review A (General Physics), 39:4854–4869, May 1989.
C. Nicolis. Stochastic aspects of climatic transitions — responses to periodic forcing. Tellus, 34:1–9, 1982.
F. W. J. Olver. Asymptotics and special functions. Computer Science and Applied Mathematics. Academic Press, a subsidiary of Harcourt Brace Jovanovich, Publishers, New York-London, 1974.
I. E. Pavlyukevich. Stochastic Resonance. PhD thesis, Humboldt-Universität, Berlin, 2002. Logos-Verlag, ISBN 3-89722-960-9.
W. B. Sellers. A global climate model based on the energy balance of the earth-atmosphere system. J. Appl. Meteor., 8:301–320, 1969.
K. Wiesenfeld and F. Jaramillo. Minireview of stocastic resonance. Chaos, 8:539–548, September 1998.
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Herrmann, S., Imkeller, P., Pavlyukevich, I. (2005). Two Mathematical Approaches to Stochastic Resonance. In: Deuschel, JD., Greven, A. (eds) Interacting Stochastic Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27110-4_15
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DOI: https://doi.org/10.1007/3-540-27110-4_15
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