Derivation of Bose-Einstein statistics from the uncertainty principle
Paul Tangney
TL;DR
The paper argues that imposing a universal lower bound on microstate-precision, $\,\Delta_Q \Delta_P > h_{?} > 0$, on all classical degrees of freedom drives the energy distribution into the Bose-Einstein form in the low-temperature limit, without requiring quantization of energy. Building on Jaynes' maximum-entropy framework, it introduces unfalsifiable statistical models and a probability-domain quantization that constrains the domain of testable distributions to partitions of phase space. By transforming each degree of freedom's Hamiltonian into an affine form (via action-angle coordinates for oscillators), the Maxwell-Boltzmann distribution is recovered as a baseline, while Bose-Einstein statistics naturally arise for sufficiently cold, weakly interacting systems and can be extended to non-oscillatory cases. The work highlights that BE statistics can emerge purely from information-theoretic considerations and a universal uncertainty bound, with potential implications for interpreting blackbody spectra, zero-point energies, and the boundary between classical and quantum descriptions. It does not claim microstates are quantized; rather, it shows that testable probability distributions acquire BE form under the stated uncertainty principle, linking classical dynamics to quantum-like statistics in a fundamental way.
Abstract
The microstate of any degree of freedom of any classical dynamical system can be represented by a point in its two dimensional phase space. Since infinitely precise measurements are impossible, a measurement can, at best, constrain the location of this point to a region of phase space whose area is finite. This paper explores the implications of assuming that this finite area is bounded from below. I prove that if the same lower bound applied to every degree of freedom of a sufficiently cold classical dynamical system, the distribution of the system's energy among its degrees of freedom would be a Bose-Einstein distribution.
