Reconstruction of the superstatistical temperature distribution from single-particle kinetic energies

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.