A New Thermodynamics, From Nuclei to Stars II
D.H.E.Gross

TL;DR
This paper presents a fundamental approach to equilibrium statistical mechanics using the microcanonical ensemble, applicable to both extensive and non-extensive systems like nuclei and stars, without relying on traditional thermodynamic assumptions.
Contribution
It introduces a geometrical, information-theory-independent definition of entropy that applies universally, enabling accurate analysis of small and non-extensive systems and their phase transitions.
Findings
Defines entropy via phase space volume without thermodynamic limits
Successfully describes phase transitions in small systems
Addresses systems beyond traditional Boltzmann-Gibbs thermodynamics
Abstract
Equilibrium statistics of Hamiltonian systems is correctly described by the microcanonical ensemble. Classically this is the manifold of all points in the N-body phase space with the given total energy. Due to Boltzmann's principle, e^S=tr(\delta(E-H)), its geometrical size is related to the entropy S(E,N,...). This definition does not invoke any information theory, no thermodynamic limit, no extensivity, and no homogeneity assumption, as are needed in conventional (canonical) thermo-statistics. Therefore, it describes the equilibrium statistics of extensive as well of non-extensive systems. Due to this fact it is the general and fundamental definition of any classical equilibrium statistics. It can address nuclei and astrophysical objects as well. As these are not described by the conventional extensive Boltzmann-Gibbs thermodynamics, this is a mayor achievement of statistical…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStatistical Mechanics and Entropy · Advanced Thermodynamics and Statistical Mechanics · Theoretical and Computational Physics
