Bose-Einstein and Fermi-Dirac distributions in nonextensive quantum statistics: Exact and interpolation approaches
Hideo Hasegawa (Tokyo Gakugei Univ.)

TL;DR
This paper investigates generalized quantum distributions in nonextensive statistics, proposing an interpolation approximation that aligns well with exact methods and analyzing their implications for thermodynamic properties.
Contribution
It introduces an interpolation approximation for nonextensive quantum distributions that agrees with exact solutions and simplifies calculations of thermodynamic quantities.
Findings
Interpolation approximation matches exact results within O(q-1)
Good agreement in high- and low-temperature limits
Potentially useful for nonextensive quantum statistical calculations
Abstract
Generalized Bose-Einstein (BE) and Fermi-Dirac (FD) distributions in nonextensive quantum statistics have been discussed by the maximum-entropy method (MEM) with the optimum Lagrange multiplier based on the exact integral representation [Rajagopal, Mendes, and Lenzi, Phys. Rev. Lett. {\bf 80}, 3907 (1998)]. It has been shown that the expansion in the exact approach agrees with the result obtained by the asymptotic approach valid for . Model calculations have been made with a uniform density of states for electrons and with the Debye model for phonons. Based on the result of the exact approach, we have proposed the {\it interpolation approximation} to the generalized distributions, which yields results in agreement with the exact approach within and in high- and low-temperature limits. By using the four methods of the exact, interpolation, factorization and…
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 · Complex Systems and Time Series Analysis · Benford’s Law and Fraud Detection
