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On the TASEP with Second Class Particles

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dc.contributor.author Lee, Eunghyun
dc.date.accessioned 2020-03-16T04:58:07Z
dc.date.available 2020-03-16T04:58:07Z
dc.date.issued 2018-01-18
dc.identifier.citation Lee, E. (2018). On the TASEP with Second Class Particles. Symmetry, Integrability and Geometry: Methods and Applications. https://doi.org/10.3842/sigma.2018.006 en_US
dc.identifier.issn 1815-0659
dc.identifier.other https://www.emis.de/journals/SIGMA/2018/006/
dc.identifier.uri https://www.emis.de/journals/SIGMA/2018/006/
dc.identifier.uri http://nur.nu.edu.kz/handle/123456789/4524
dc.description https://www.emis.de/journals/SIGMA/2018/006/sigma18-006.pdf en_US
dc.description.abstract In this paper we study some conditional probabilities for the totally asymmetric simple exclusion processes (TASEP) with second class particles. To be more specific, we consider a finite system with one first class particle and N−1 second class particles, and we assume that the first class particle is initially at the leftmost position. In this case, we find the probability that the first class particle is at x and it is still the leftmost particle at time t. In particular, we show that this probability is expressed by the determinant of an N×N matrix of contour integrals if the initial positions of particles satisfy the step initial condition. The resulting formula is very similar to a known formula in the (usual) TASEP with the step initial condition which was used for asymptotics by Nagao and Sasamoto [Nuclear Phys. B 699 (2004), 487-502]. en_US
dc.language.iso en en_US
dc.publisher National Academy of Science of Ukraine en_US
dc.relation.ispartofseries Symmetry, Integrability and Geometry: Methods and Applications;
dc.rights Attribution-NonCommercial-ShareAlike 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/us/ *
dc.subject TASEP en_US
dc.subject second class particles en_US
dc.subject Bethe ansatz en_US
dc.title On the TASEP with Second Class Particles en_US
dc.type Article en_US
workflow.import.source science


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