- Access by Xinjiang University
Temporal disorder in up-down symmetric systems
Phys. Rev. E 85, 051125 – Published 16 May, 2012
DOI: https://doi.org/10.1103/PhysRevE.85.051125
Abstract
The effect of temporal disorder on systems with up-down symmetry is studied. In particular, we analyze two well-known families of phase transitions—the Ising and the generalized voter universality classes—and scrutinize the consequences of placing them under fluctuating global conditions. We observe that variability of the control parameter induces in both classes “temporal Griffiths phases” (TGPs). These recently uncovered phases are analogous to standard Griffiths phases appearing in systems with quenched spatial disorder, but where the roles of space and time are exchanged. TGPs are characterized by broad regions in parameter space in which (i) mean first-passage times scale algebraically with system size, and (ii) the system response (e.g., susceptibility) diverges. Our results confirm that TGPs are quite robust and ubiquitous in the presence of temporal disorder. Possible applications of our results to examples in ecology are discussed.
Article Text
References (38)
- H. Hinrichsen, Adv. Phys. 49, 815 (2000).
- G. Ódor, Rev. Mod. Phys. 76, 663 (2004).
- G. Grinstein and M. A. Muñoz, in Fourth Granada Lectures in Computational Physics, edited by P. Garrido and J. Marro, Lecture Notes in Physics, Vol. 493 (Springer, Berlin 1997), p. 223.
- J. Marro and R. Dickman, Nonequilibrium Phase Transitions in Lattice Models (Cambridge University Press, Cambridge, 1999).
- O. Al Hammal, H. Chaté, I. Dornic, and M. A. Muñoz, Phys. Rev. Lett. 94, 230601 (2005).
- I. Dornic, H. Chaté, J. Chave, and H. Hinrichsen, Phys. Rev. Lett. 87, 045701 (2001).
- A. Lipowski and M. Droz, Phys. Rev. E 65, 056114 (2002).
- M. Droz, A. L. Ferreira, and A. Lipowski, Phys. Rev. E 67, 056108 (2003).
- F. Vazquez and C. López, Phys. Rev. E 78, 061127 (2008).
- D. I. Russell and R. A. Blythe, Phys. Rev. Lett. 106, 165702 (2011).
- P. Clifford and A. Sudbury, Biometrika 60, 581 (1973).
- R. Durrett and S. Levin, J. Theor. Biol. 179, 119 (1996).
- G. J. Baxter, A. Blythe, and A. J. McKane, Math. Biosci. 209, 124 (2007).
- C. Castellano, S. Fortunato, and V. Loreto, Rev. Mod. Phys. 81, 591 (2009).
- O. A. Pinto and M. A. Muñoz, PLoS ONE 6, e21946 (2011).
- D. M. Abrams and S. H. Strogatz, Nature (London) 424, 900 (2003).
- J. Calabrese, F. Vázquez, C. López, M. San Miguel, and V. Grimm, Am. Nat. 175, E44 (2010).
- E. G. Leigh Jr., J. Theor. Biol. 90, 213 (1981).
- P. Chesson, Annual Review of Ecology, Evolution, and Systematics 31, 343 (2000).
- F. Vazquez, C. López, J. M. Calabrese, and M. A. Muñoz, J. Theor. Biol. 264, 360 (2010).
- F. Borgogno, P. D'Odorico, F. Laio, and L. Ridolfi, Rev. Geophys. 47, RG1005 (2009).
- I. Jensen, Phys. Rev. Lett. 77, 4988 (1996).
- J.J. Alonso and M.A. Muñoz, Europhys. Lett. 56, 485 (2001).
- A. Kamenev, B. Meerson, and B. Shklovskii, Phys. Rev. Lett. 101, 268103 (2008).
- F. Vazquez, J. A. Bonachela, C. López, and M. A. Muñoz, Phys. Rev. Lett. 106, 235702 (2011).
- T. Vojta, J. Phys. A: Math. Gen. 39, R143 (2006).
- A. J. Bray, Phys. Rev. Lett. 59, 586 (1987).
- N. G. Van Kampen, Stochastic Processes in Physics and Chemistry (North-Holland, Amsterdam, 2004).
- C. W. Gardiner, Handbook of Stochastic Methods (Springer-Verlag, Berlin, 1985).
- W. Horsthemke and R. Lefever, Noise-Induced Transitions (Springer-Verlag, Berlin, 1984).
- R. J. Glauber, Journal of Math. Phys. 4, 2 (1963).
- I. Dornic, H. Chaté, and M. A. Muñoz, Phys. Rev. Lett. 94, 100601 (2005).
- H.-O. Heuer, Phys. A 26, L333 (1993).
- G. Grinstein, M. A. Muñoz, and Y. Tu, Phys. Rev. Lett. 76, 4376 (1996); Y. Tu, G. Grinstein, and M. A. Muñoz, ibid. 78, 274 (1997); W. Genovese and M. A. Muñoz, Phys. Rev. E 60, 69 (1999).
- M. A. Muñoz, Multiplicative Noise in Non-Equilibrium Phase Transitions: A Tutorial. Advances in Condensed Matter and Statistical Mechanics (Nova Science Publishers, Hauppauge, 2004).
- C. Castellano, M. A. Muñoz, and R. Pastor-Satorras, Phys. Rev. E 80, 041129 (2009).
- For a fully connected network the number of neighbors has no meaning. However, the MF limit of the model refers to the use of the probability [Eq. (23)].
- L. Canet, H. Chaté, B. Delamotte, I. Dornic, and M. A. Muñoz, Phys. Rev. Lett. 95, 100601 (2005).