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Black Hole Entropy from Dimensional Information: A First-Principles Derivation

Authors: Todd, Ian;

Black Hole Entropy from Dimensional Information: A First-Principles Derivation

Abstract

We present a novel derivation of the Bekenstein-Hawking black hole entropy formula using the dimensional information framework. By analyzing event horizons as coherence boundaries with maximal entropy-flow continuity and perfect coherence across the global cut, we demonstrate that the maximum sustainable dimensional information at a black hole horizon scales linearly with its area. Working from thermodynamic coherence constraints at causal boundaries, and without invoking specific microscopic models, we derive the Bekenstein-Hawking entropy scaling as a natural consequence of dimensional information theory, recovering the scaling S_BH = k_B A / (4 ℓ_P^2) as the expected information-theoretic capacity of the horizon. This approach provides a causal-thermodynamic explanation for the area law and situates black hole thermodynamics within a broader framework of dimensional information theory applicable to all causal boundaries in thermodynamically open systems.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average