Large Deviations in Switching Diffusion: Dynamical Condensation and Diffusivity BBP Transition
Abstract
We consider a Brownian particle with position x(t) whose diffusivity changes at Poissonian switching times, with each new value drawn independently from a bounded distribution W(D). Assuming W(D)∼ C(Dmax−D)ν near its upper edge, we study the large-deviation regime x(t)≃ yt with y = O(1) at large times, far beyond the typical diffusive scale. In a recent work Guéneau et al., Phys. Rev. Lett. 135, 067102 (2025), the rate function of the displacement was obtained through a renewal approach and it was shown to display several singularities. In this paper, we carry out a detailed study of the corresponding dynamical phase transitions and trajectory condensation. For moderate y, the imposed displacement is shared among O(t) displacement increments accumulated during successive intervals between switches. Beyond a first threshold y-, one interval produces an increment of order t carrying a finite fraction of the total displacement, signaling dynamical condensation. For ν>1, a second threshold y+>y- is reached when this macroscopic increment carries asymptotically the entire imposed displacement, whereas for 0<ν≤1 the two thresholds merge, y-=y+. In turn, the moment-generating function of x(t) displays a complementary transition from collective contributions of many diffusivities to selection of states near Dmax. We show that this transition is governed by the same edge mechanism as the Baik-Ben Arous-Péché (BBP) transition, indicating that diffusivity selection and trajectory condensation are two manifestations of the same phenomenon. We validate our predictions for the dynamical condensation against numerical results from rare event sampling.
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BibTeX
@misc{gueneau2026large,
title = {{Large Deviations in Switching Diffusion: Dynamical Condensation and Diffusivity BBP Transition}},
author = {Mathis Guéneau and Timo Schorlepp and Satya N. Majumdar and Gregory Schehr},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.25072},
eprint = {2610.25072},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.25072},
}
RIS
TY - UNPB
TI - Large Deviations in Switching Diffusion: Dynamical Condensation and Diffusivity BBP Transition
AU - Mathis Guéneau
AU - Timo Schorlepp
AU - Satya N. Majumdar
AU - Gregory Schehr
PY - 2026
DA - 2026/10/18
PB - arXiv
DO - 10.48550/arxiv.2610.25072
UR - https://arxiv.org/abs/2610.25072
AB - We consider a Brownian particle with position x(t) whose diffusivity changes at Poissonian switching times, with each new value drawn independently from a bounded distribution W(D). Assuming W(D)∼ C(D_max−D)^ν near its upper edge, we study the large-deviation regime x(t)≃ yt with y = O(1) at large times, far beyond the typical diffusive scale. In a recent work Guéneau et al., Phys. Rev. Lett. 135, 067102 (2025), the rate function of the displacement was obtained through a renewal approach and it was shown to display several singularities. In this paper, we carry out a detailed study of the corresponding dynamical phase transitions and trajectory condensation. For moderate y, the imposed displacement is shared among O(t) displacement increments accumulated during successive intervals between switches. Beyond a first threshold y_-, one interval produces an increment of order t carrying a finite fraction of the total displacement, signaling dynamical condensation. For ν>1, a second threshold y_+>y_- is reached when this macroscopic increment carries asymptotically the entire imposed displacement, whereas for 0<ν≤1 the two thresholds merge, y_-=y_+. In turn, the moment-generating function of x(t) displays a complementary transition from collective contributions of many diffusivities to selection of states near D_max. We show that this transition is governed by the same edge mechanism as the Baik-Ben Arous-Péché (BBP) transition, indicating that diffusivity selection and trajectory condensation are two manifestations of the same phenomenon. We validate our predictions for the dynamical condensation against numerical results from rare event sampling.
ER -