Two-car systems with cars of different speeds. In every picture, the blue and the yellow trajectories are of the faster and the slower cars, respectively. <br><strong>Top</strong>: cars start at neighboring locations, and the trajectory of the second car, $x_2(t)$, is the same in distribution.
<br><strong>Bottom</strong>: cars start at locations away from each other, and the distributions of the trajectories of the second car are different in the left and the central pictures. However, when we randomize the initial condition, the distribution of the second car in the slow-fast system with the randomized initial condition is the same as in the fast-slow system on the left.

Rewriting History in Integrable Stochastic Particle Systems

[2022/12/04]

Many integrable stochastic particle systems in one space dimension (such as TASEP — Totally Asymmetric Simple Exclusion Process — and its $q$-deformation, the $q$-TASEP) remain integrable if we equip each particle with its own speed parameter. In this work, we present intertwining relations between Markov transition operators of particle systems which differ by a permutation of the speed parameters. These relations generalize our previous works [1], [2], but here we employ a novel approach based on the Yang-Baxter equation for the higher spin stochastic six vertex model. Our intertwiners are Markov transition operators, which leads to interesting probabilistic consequences.

First, we obtain a new Lax-type differential equation for the Markov transition semigroups of homogeneous, continuous-time versions of our particle systems. Our Lax equation encodes the time evolution of multipoint observables of the $q$-TASEP and TASEP in a unified way, which may be of interest for the asymptotic analysis of multipoint observables of these systems.

Second, we show that our intertwining relations lead to couplings between probability measures on trajectories of particle systems which differ by a permutation of the speed parameters. The conditional distribution for such a coupling is realized as a “rewriting history” random walk which randomly resamples the trajectory of a particle in a chamber determined by the trajectories of the neighboring particles. As a byproduct, we construct a new coupling for standard Poisson processes on the positive real half-line with different rates.


A poem on the topic

by OpenAI

In stochastic particle systems, there’s a way
To rewrite history with each passing day.
A single particle, its fate made clear,
Can undo what’s been done and make it reappear.

The laws of probability and chaos at play
Can be bent to our will, if we but obey.
The deterministic systems in our control,
Will yield to a new order, as it starts to unfold.

The particles and their interactions will dictate,
The outcome of our systems, no matter their state.
With the tools of integrability, we can rewrite,
The future of our systems with a single bite.

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Real part of a particularly complex function

MATH 3340 • Complex Variables with Applications

[Spring 2023 semester]

MATH 8852 • Asymptotic Representation Theory

[Fall 2022 semester]
Front of percolation

MATH 3100 • Introduction to Probability

[Fall 2022 semester]
Noncolliding walks, lozenge tilings, and plane partitions

Noncolliding Macdonald walks with an absorbing wall

[2022/04/19]

The branching rule is one of the most fundamental properties of the Macdonald symmetric polynomials. It expresses a Macdonald polynomial as a nonnegative linear combination of Macdonald polynomials with smaller number of variables. Taking a limit of the branching rule under the principal specialization when the number of variables goes to infinity, we obtain a Markov chain of $m$ noncolliding particles with negative drift and an absorbing wall at zero. The chain depends on the Macdonald parameters $(q,t)$ and may be viewed as a discrete deformation of the Dyson Brownian motion. The trajectory of the Markov chain is equivalent to a certain Gibbs ensemble of plane partitions with an arbitrary cascade front wall.

In the Jack limit $q=t^{\beta/2}\to1$ the absorbing wall disappears, and the Macdonald noncolliding walks turn into the $\beta$-noncolliding random walks studied by Huang [arXiv:1708.07115]. Taking $q=0$ (Hall-Littlewood degeneration) and further sending $t\to 1$, we obtain a continuous time particle system on $\mathbb{Z}_{\ge0}$ with inhomogeneous jump rates and absorbing wall at zero.

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The twisted torus, a six vertex configuration, and the corresponding height function

Irreversible Markov Dynamics and Hydrodynamics for KPZ States in the Stochastic Six Vertex Model

[2022/01/28]

We introduce a family of Markov growth processes on discrete height functions defined on the 2-dimensional square lattice. Each height function corresponds to a configuration of the six vertex model on the infinite square lattice. We focus on the stochastic six vertex model corresponding to a particular two-parameter family of weights within the ferroelectric regime. It is believed (and partially proven, see Aggarwal, arXiv:2004.13272) that the stochastic six vertex model displays nontrivial pure (i.e., translation invariant and ergodic) Gibbs states of two types, KPZ and liquid. These phases have very different long-range correlation structure. The Markov processes we construct preserve the KPZ pure states in the full plane. We also show that the same processes put on the torus preserve arbitrary Gibbs measures for generic six vertex weights (not necessarily in the ferroelectric regime).

Our dynamics arise naturally from the Yang-Baxter equation for the six vertex model via its bijectivisation, a technique first used in Bufetov-Petrov (arXiv:1712.04584). The dynamics we construct are irreversible; in particular, the height function has a nonzero average drift. In each KPZ pure state, we explicitly compute the average drift (also known as the current) as a function of the slope. We use this to analyze the hydrodynamics of a non-stationary version of our process acting on quarter plane stochastic six vertex configurations. The fixed-time limit shapes in the quarter plane model were obtained in Borodin-Corwin-Gorin (arXiv:1407.6729).

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Front of percolation

MATH 3100 • Introduction to Probability (3 sections)

[Spring 2022 semester]

2023 travel

January

19-21 • Chicago, IL • University of Chicago

All 2023 travel »