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Soliton Decomposition of the Box-Ball System

Published online by Cambridge University Press:  03 September 2021

Pablo A. Ferrari
Affiliation:
Instituto de Investigaciones Matemáticas Luis A. Santaló, Argentinian National Research Council at the University of Buenos Aires, 1428 Buenos Aires, Argentina
Chi Nguyen
Affiliation:
Instituto de Investigaciones Matemáticas Luis A. Santaló, Argentinian National Research Council at the University of Buenos Aires, 1428 Buenos Aires, Argentina Department of Information Technology, Ho Chi Minh City University of Transport, 2 Vo Oanh St., Binh Thanh Dist., Ho Chi Minh City, Vietnam
Leonardo T. Rolla
Affiliation:
Instituto de Investigaciones Matemáticas Luis A. Santaló, Argentinian National Research Council at the University of Buenos Aires, 1428 Buenos Aires, Argentina Department of Statistics, University of Warwick, CV4 7AL, United Kingdom
Minmin Wang
Affiliation:
Instituto de Investigaciones Matemáticas Luis A. Santaló, Argentinian National Research Council at the University of Buenos Aires, 1428 Buenos Aires, Argentina Department of Mathematics, University of Sussex, BN1 9QH, United Kingdom

Abstract

BBS dynamics for independent and identically distributed initial configuration with density 0.25. Time is going down. Straight red lines are deterministic and computed using Theorem 1.2. (High resolution, color online.)

The box-ball system (BBS) was introduced by Takahashi and Satsuma as a discrete counterpart of the Korteweg-de Vries equation. Both systems exhibit solitons whose shape and speed are conserved after collision with other solitons. We introduce a slot decomposition of ball configurations, each component being an infinite vector describing the number of size k solitons in each k-slot. The dynamics of the components is linear: the kth component moves rigidly at speed k. Let $\zeta $ be a translation-invariant family of independent random vectors under a summability condition and $\eta $ be the ball configuration with components $\zeta $. We show that the law of $\eta $ is translation invariant and invariant for the BBS. This recipe allows us to construct a large family of invariant measures, including product measures and stationary Markov chains with ball density less than $\frac {1}{2}$. We also show that starting BBS with an ergodic measure, the position of a tagged k-soliton at time t, divided by t converges as $t\to \infty $ to an effective speed $v_k$. The vector of speeds satisfies a system of linear equations related with the generalised Gibbs ensemble of conservative laws.

Information

Type
Probability
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press
Figure 0

Figure 1.1 Applying the Takahashi–Satsuma algorithm to a sample configuration. Dots represent records. On the left we have the resulting word after successive iterations. Identified solitons are shown in bold once and then with a color corresponding to their size. The algorithm is applied to each excursion separately, so the rightmost $1$-soliton in the picture is ignored by this instance of the procedure. (color online)

Figure 1

Figure 1.2 Here we show $ I(\gamma ) $ in an example with nine records, a 5-soliton, a 4-soliton, two 3-solitons, two 2-solitons and two 1-solitons, with one color for each size. In this example, a 1-soliton is contained in a 2-soliton, both 2-solitons are contained in the 5-soliton and both 3-solitons are contained in the 4-soliton. $ I(\gamma ) $ is underlined with the same color as $ \gamma $, and black zeros are records. (color online)

Figure 2

Figure 1.3 Simulation for an i.i.d. configuration with density $0.15$. The transparent red lines have deterministic slopes computed by Theorem 1.2, which have been manually shifted so that they would overlay a soliton. This window covers 2000 sites and 140 time steps going downwards and has been stretched vertically by a factor of $5$. The figure on the first page is the same except for the density. (high resolution, color online)

Figure 3

Figure 1.4 Simulation for $(\rho _k)_k=(.006,.005,.1,.003,0,0,0,\dots )$. The initial configuration was obtained by first appending one k-soliton with probability $\rho _k$ after each record and then applying T a number of times in order to mix. As in Figure 1.3, it is a 2000 x 140 window stretched by 5, and red lines are deterministic. (high resolution, color online)

Figure 4

Figure 2.1 Time evolution of a walk under seven iterations of T. This example has four solitons, of size 7, 5, 3 and 1. Different colors are used to highlight their conservation. To facilitate view, we have shifted the walk at time t by t units down. (color online)

Figure 5

Figure 2.2 A red $ 3 $-soliton can be appended to a blue $ 5 $-soliton in $ 2 \times (5-3) = 4 $ different places, represented by blue dots. It is also possible to append it to records, represented by black dots. Attempting to insert a $ 3 $-soliton at a site not marked by a dot will result in erroneous soliton identification. For instance, the 3-soliton in the middle bottom plot should actually start three sites earlier, and in the right bottom plot we should have a 2- and 6-soliton instead of 3 and 5. The green crosses indicate that the coloring is inconsistent with the procedure shown in Figure 1.1. (color online)

Figure 6

Figure 2.3 Slot configuration of a walk $\xi $. Different colors correspond to different solitons; records are painted in black. For each site, the number of dots below it indicates its level in slots: k dots indicate a k-slot. (color online)

Figure 7

Figure 2.4 An illustration of how the solitons are nested inside bigger solitons via slots, in the same sample configuration as in Figure 2.3. Solitons are represented by squares and slots by circles. For each $k \geqslant 1$, each slot with index $m\geqslant k$ is a k-slot. We say it is the nth k-slot, where n is determined by counting how many k-slots appear before it in the depth-first order, and the counting starts from the 0th k-slot present at record 0.

Figure 8

Figure 2.5 Reconstruction algorithm for a single excursion. This example is obtained using the field $\zeta $ shown in Figure 2.6.

Figure 9

Figure 2.6 Reconstruction of $\xi $ from $\zeta $. In the lower part we show records $-2$ to $2$ in boldface and the excursions between them. Above we show the parts of the field $\zeta $ used in the reconstruction of $\varepsilon ^{-2},\varepsilon ^{-1},\varepsilon ^{0},\varepsilon ^{1}$. Reconstruction of $\varepsilon ^0$ was shown in Figure 2.5 and $\varepsilon ^{1},\varepsilon ^{-1},\varepsilon ^{-2}$ is shown in Figure 2.7.

Figure 10

Figure 2.7 Reconstruction algorithm for other excursions. The procedure is the same as in Figure 2.5 but all of the intermediate steps are omitted.

Figure 11

Figure 3.1 We depict $T\xi $ below $\xi $. This example illustrates the various situations discussed in the proof of Theorem 3.1. For instance, the $5$-soliton (in pink) stays put and is nested in the $6$-soliton (in blue). The displacement of the 6-soliton brings five ‘new’ 5-slots to the left of the 5-soliton. (color online)

Figure 12

Figure 7.1 Example showing conservation of solitons by splitting space in three parts: rising, falling and remainder. After applying T, the configurations on the rising and falling parts are flipped, the smaller solitons are conserved and flipped and the biggest soliton moves forward and will have its tail in the remainder part. Applying T to the remainder part conserves solitons by induction.