Reconstruction of the superstatistical temperature distribution from single-particle kinetic energies
Year: 2026
Type: preprint
Abstract: The framework of superstatistics can be used to describe non-Maxwellian single-particle velocity distributions as mixtures of Maxwellian distributions, where the weight is imposed by a probability density associated to the inverse temperature $β= 1/(k_B T)$. Among the typical model choices for $β$ are gamma, inverse gamma and lognormal distributions, sometimes called the universality classes of superstatistics. Given a set of observed velocities, however, there is no direct method to determine the underlying temperature distribution, as temperature itself is not a phase-space observable, and numerically inverting the Laplace transform is an unreliable process. In this work, we show that Jaynes' principle of maximum entropy can be used to successfully reconstruct the inverse temperature distribution from data, by using the logarithmic moments of the kinetic energy as constraints. Although the kinetic energy distributions for the three universality classes are almost indistinguishable at the same mean and variance of $β$, the method correctly discriminates between them.
Source: arXiv (Cornell University)
Author: Davis, Sergio
Institutions: Universidad Andrés Bello, Comisión Chilena de Energía Nuclear
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7213316396