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AN IMPROVED LOWER BOUND FOR THE CRITICAL PARAMETER OF STAVSKAYA’S PROCESS

Published online by Cambridge University Press:  13 May 2020

ALEX D. RAMOS
Affiliation:
Department of Statistics, Universidade Federal de Pernambuco, Recife, PE, 50740-540, Brazil email alex@de.ufpe.br
CALITÉIA S. SOUSA
Affiliation:
Department of Statistics, Universidade Federal de Pernambuco, Recife, PE, 50740-540, Brazil email caliteia@de.ufpe.br
PABLO M. RODRIGUEZ*
Affiliation:
Department of Statistics, Universidade Federal de Pernambuco, Recife, PE, 50740-540, Brazil email pablo@de.ufpe.br
PAULA CADAVID
Affiliation:
Universidade Federal do ABC, Santo André, SP, 09210-580, Brazil email pacadavid@gmail.com

Abstract

We consider Stavskaya’s process, which is a two-state probabilistic cellular automaton defined on a one-dimensional lattice. The state of any vertex depends only on itself and on the state of its right-adjacent neighbour. This process was one of the first multicomponent systems with local interaction for which the existence of a kind of phase transition has been rigorously proved. However, the exact localisation of its critical value remains as an open problem. We provide a new lower bound for the critical value.

Type
Research Article
Copyright
© 2020 Australian Mathematical Publishing Association Inc.

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Footnotes

This work has been partially supported by FAPESP (2017/10555-0), CNPq (Grant 304676/2016-0) and CAPES (under the Program MATH-AMSUD/CAPES 88881.197412/2018-01).

References

Depoorter, J. and Maes, C., ‘Stavskaya’s measure is weakly Gibbsian’, Markov Process. Related Fields 12 (2006), 791804.Google Scholar
Gantmacher, F. R., Applications of the Theory of Matrices (Interscience Publishers, New York, 1959).Google Scholar
Harris, T. E., ‘Contact interactions on a lattice’, Ann. Probab. 2(6) (1974), 969988.CrossRefGoogle Scholar
Liggett, T. M., Interacting Particle Systems (Springer, Berlin, 1985).CrossRefGoogle Scholar
Mendonça, J., ‘Monte Carlo investigation of the critical behavior of Stavskaya’s probabilistic cellular automaton’, Phys. Rev. E 83(1) (2011), 012102.Google ScholarPubMed
Ponselet, L., Phase Transitions in Probabilistic Cellular Automata, PhD Thesis, Université catholique de Louvain, 2013.Google Scholar
Taggi, L., ‘Critical probabilities and convergence time of percolation probabilistic cellular automata’, J. Stat. Phys. 159(4) (2015), 853892.CrossRefGoogle Scholar
Toom, A. L., ‘A family of uniform nets of formal neurons’, Sov. Math. Dokl. 9(6) (1968).Google Scholar
Toom, A., Contornos, Conjuntos Convexos e Autômatos Celulares (in Portuguese), 23o Colóquio Brasileiro de Matemática (IMPA, Rio de Janeiro, 2001).Google Scholar
Toom, A., Ergodicity of Cellular Automata, Tartu University, Estonia, 2013. Available online at http://math.ut.ee/emsdk/intensiivkursused/TOOM-TARTU-3.pdf.Google Scholar
Toom, A., Vasilyev, N., Stavskaya, O., Mityushin, L., Kurdyumov, G. and Pirogov, S., ‘Discrete local Markov systems’, in: Stochastic Cellular Systems: Ergodicity, Memory, Morphogenesis, Nonlinear Science: Theory and Applications (eds. Dobrushin, R., Kryukov, V. and Toom, A.) (Manchester University Press, Manchester, 1990).Google Scholar