Hyperstatistics

TL;DR

Hyperstatistics introduces a generalized framework for complex systems where traditional Boltzmann-Gibbs statistics fails, providing analytical expressions and demonstrating broad applicability across physical and experimental systems.

Contribution

It develops a new hyperstatistics approach that preserves nonadditive $q$-entropy and derives analytical $q$-generalized Boltzmann factors for various distributions.

Findings

$B_q$ reduces to a $q$-exponential function for all distributions studied.

Demonstrated hyperstatistics with experiments on capacitor discharge and cryostat pressure decay.

Applied hyperstatistics to high-energy collision data and turbulent systems.

Abstract

We propose a general approach, named by us hyperstatistics, to treat complex systems, in which Boltzmann-Gibbs statistics breaks down in domains of the system. Hyperstatistics preserves the concavity of nonadditive $q$-entropy. We obtain analytical closed-form expressions for the here proposed $q$-generalized Boltzmann factor $B_q$ considering uniform, $\gamma$, Log-normal, F, and the $q$-$\gamma$ probability distribution functions. Remarkably, for all investigated distribution functions, $B_q$ reduces to a $q$-exponential-type function. To demonstrate the applicability of hyperstatistics, we use a table top experiment of the discharge of a capacitor considering $\gamma$-distributed relaxation times, the pressure decay over time associated with the pumping of $^4$He lines of a closed cycle cryostat, midrapidity data for $p$-Pb collisions at the LHC, as well as data set for acceleration distribution in turbulent systems. Furthermore, we deduce the power-law-like dielectric response using the $q$-$\gamma$-distribution function. Our proposal is applicable to systems with inherent non-Boltzmann-Gibbsian statistics in domains of the system.