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The peeling process on random planar maps coupled to an O(n) loop model (with an appendix by Linxiao Chen)

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arxiv 1809.02012 v1 pith:ANB55BXC submitted 2018-09-06 math.PR math-phmath.COmath.MP

The peeling process on random planar maps coupled to an O(n) loop model (with an appendix by Linxiao Chen)

classification math.PR math-phmath.COmath.MP
keywords processesrandommapsmodelmarkovpositiveprocessangle
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We extend the peeling exploration introduced in arxiv:1506.01590 to the setting of Boltzmann planar maps coupled to a rigid $O(n)$ loop model. Its law is related to a class of discrete Markov processes obtained by confining random walks to the positive integers with a new type of boundary condition. As an application we give a rigorous justification of the phase diagram of the model presented in arXiv:1106.0153. This entails two results pertaining to the so-called fixed-point equation: the first asserts that any solution determines a well-defined model, while the second result, contributed by Chen in the appendix, establishes precise existence criteria. A scaling limit for the exploration process is identified in terms of a new class of positive self-similar Markov processes, going under the name of ricocheted stable processes. As an application we study distances on loop-decorated maps arising from a particular first passage percolation process on the maps. In the scaling limit these distances between the boundary and a marked point are related to exponential integrals of certain L\'evy processes. The distributions of the latter can be identified in a fairly explicit form using machinery of positive self-similar Markov processes. Finally we observe a relation between the number of loops that surround a marked vertex in a Boltzmann loop-decorated map and the winding angle of a simple random walk on the square lattice. As a corollary we give a combinatorial proof of the fact that the total winding angle around the origin of a simple random walk started at $(p,p)$ and killed upon hitting $(0,0)$ normalized by $\log p$ converges in distribution to a Cauchy random variable.

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