Skip to content
DraconiticResearch
Search Sources API
197,768 studies31 sourcesUpdated 54 min ago
PreprintarXiv licencearXiv

Anomalous current fluctuations in the stochastic XNOR hopping model

Abstract

We consider fluctuations of the spin current in the stochastic XNOR process, a kinetically constrained hopping model in one spatial dimension. We formulate three conjectures with explicit amplitudes for the long-time current distributions: a Gaussian limit on the t1/4 scale for homogeneous initial states with nonzero magnetization, a half-normal limit on the t1/4 scale for domain-wall states with opposite magnetizations, and an M-Wright limit on the t1/8 scale for homogeneous initial states at zero magnetization. Earlier work identified the tracer mechanism and anticipated the scaling exponents and limiting shapes in the domain-wall and zero-magnetization settings. Starting from the microscopic XNOR dynamics, we predict the missing amplitudes for the continuous-time process and extend the picture to homogeneous biased initial data. Simulations provide numerical support for all three conjectures without fitted parameters. A separate mathematical companion paper presents an extensively AI-generated candidate proof. Generative-AI tools were used in the research and writing workflow.

Related

  1. Exact anomalous current fluctuations in the strong-anisotropy limit of the XXZ chain
    Taiki Ishiyama, Taiga Kurose, Kazuya Fujimoto +1
    arXivPreprintarXiv licenceFull textMathematical physicsAtomic, molecular & optical physicsarXiv
  2. Universal classical and quantum fluctuations in the large deviations of current of noisy quantum systems: Quantum symmetric simple exclusion and inclusion processes
    Mathias Albert, Denis Bernard, Tony Jin +2
    Physical Review EPeer-reviewedLicence not statedFull text10 citedMathematical physicsHALOpenAlex
  3. Gaussian fluctuations for the stochastic wave equation with drift
    Raluca M. Balan, William D. Stephenson
    arXivPreprintCC BY 4.0Full textProbabilityarXiv
  4. Density and Current Fluctuations for Run-and-Tumble Particles With and Without Interactions
    Léo Touzo, Gregory Schehr, Pierre Le Doussal
    arXivPreprintarXiv licenceFull textMathematical physicsProbabilityarXiv
  5. Fluctuations and multifractality in stochastic models of interface growth and population dynamics
    Maximilien Bernard
    arXivPreprintCC BY 4.0Full textProbabilityCondensed matterarXiv
BibTeX
@misc{pozsgay2026anomalous,
  title = {{Anomalous current fluctuations in the stochastic XNOR hopping model}},
  author = {Balázs Pozsgay},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2608.10536},
  eprint = {2608.10536},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.10536},
}
RIS
TY  - UNPB
TI  - Anomalous current fluctuations in the stochastic XNOR hopping model
AU  - Balázs Pozsgay
PY  - 2026
DA  - 2026/09/07
PB  - arXiv
DO  - 10.48550/arxiv.2608.10536
UR  - https://arxiv.org/abs/2608.10536
AB  - We consider fluctuations of the spin current in the stochastic XNOR process, a kinetically constrained hopping model in one spatial dimension. We formulate three conjectures with explicit amplitudes for the long-time current distributions: a Gaussian limit on the t^1/4 scale for homogeneous initial states with nonzero magnetization, a half-normal limit on the t^1/4 scale for domain-wall states with opposite magnetizations, and an M-Wright limit on the t^1/8 scale for homogeneous initial states at zero magnetization. Earlier work identified the tracer mechanism and anticipated the scaling exponents and limiting shapes in the domain-wall and zero-magnetization settings. Starting from the microscopic XNOR dynamics, we predict the missing amplitudes for the continuous-time process and extend the picture to homogeneous biased initial data. Simulations provide numerical support for all three conjectures without fitted parameters. A separate mathematical companion paper presents an extensively AI-generated candidate proof. Generative-AI tools were used in the research and writing workflow.
ER  -