PreprintCC BY 4.0arXiv
Generation of N-particle kappa-distributed velocities via superstatistical Gibbs sampling
Abstract
The \emph{kappa} distribution is widely used to model heavy-tailed, non-equilibrium velocity distributions in space and laboratory plasmas. Its correct use in particle-in-cell (PIC) simulations requires more than sampling the single-particle marginal: the N-particle \emph{kappa} distribution predicts non-trivial correlations between the kinetic energies of particles. Building on the superstatistical structure, we implement a Gibbs sampler whose stationary distribution is the joint distribution of the velocities and β, so that the sampled velocities follow the N-particle \emph{kappa} distribution and reproduce these correlations by construction.
Related
- Reconstruction of the superstatistical temperature distribution from single-particle kinetic energies
- Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling
- STORM: RDMA-based Monte Carlo Transport Scheme for Distributed-Memory Particle Simulations
- Particle settling in turbidity currents: concentration-gradient-driven biased sampling
- Inverter control for wind–solar–storage based on distributed generation and virtual synchronous machines
BibTeX
@misc{herrerafuenzalida2026generation,
title = {{Generation of $N$-particle kappa-distributed velocities via superstatistical Gibbs sampling}},
author = {Leonardo Herrera_Fuenzalida and Sergio Davis},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.25487},
eprint = {2610.25487},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.25487},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Generation of N-particle kappa-distributed velocities via superstatistical Gibbs sampling
AU - Leonardo Herrera_Fuenzalida
AU - Sergio Davis
PY - 2026
DA - 2026/10/18
PB - arXiv
DO - 10.48550/arxiv.2610.25487
UR - https://arxiv.org/abs/2610.25487
AB - The \emph{kappa} distribution is widely used to model heavy-tailed, non-equilibrium velocity distributions in space and laboratory plasmas. Its correct use in particle-in-cell (PIC) simulations requires more than sampling the single-particle marginal: the N-particle \emph{kappa} distribution predicts non-trivial correlations between the kinetic energies of particles. Building on the superstatistical structure, we implement a Gibbs sampler whose stationary distribution is the joint distribution of the velocities and β, so that the sampled velocities follow the N-particle \emph{kappa} distribution and reproduce these correlations by construction.
ER -