Abstract
We present a weighted frame operator framework for analyzing spectral stability near black hole horizons, using torus-compactified Rindler spacetime as a mathematically rigorous toy model. Employing horizon-adapted weights $w(x) = x^\alpha$ encoding surface gravity $\kappa$ (with $\alpha \sim \kappa / 2\pi$), we establish (1) uniform boundedness with explicit $\Gamma(\alpha + 1)$ estimates, (2) high-frequency suppression at the rate $|n|^{-\alpha}$, and (3) controlled pseudo-inversion with quantitative error bounds. These results provide a provable analytic foundation for horizon-modified quasi-normal modes and for the stabilization of local energy flux. Extensions to more general geometries and weights are outlined, with the core methods accessible to both mathematicians and physicists. We also discuss possible connections between these mathematical findings and open questions in black hole thermodynamics, quantum information, and the information paradox, with an emphasis on the interpretive nature of such links.
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Source Code and Publication Figures – Rindler Black Hole Frame Operator Model
Description
This archive contains the complete source code, Google Colab computation notebooks, publication-quality figures, and all output data supporting the manuscript:
Materials included:
Python source code (NumPy, SciPy, Matplotlib)
Fully reproducible Colab and Jupyter notebooks for all major figures and results
Publication-ready vector figures (PDF)
Raw computational outputs and data tables (CSV, TXT)
LICENSE (MIT, code)
All code execution and figure generation were performed on Google Colab using Python 3.9.16, NumPy 1.24.3, SciPy 1.10.1, and Matplotlib 3.7.1. No local desktop environment is needed—just a browser and Google account.
The provided resources allow any researcher to directly reproduce and extend all results from the associated manuscript, including spectral entropy validation, pseudo-inverse diagnostics, and horizon model recovery tests.
For citation, please reference both this Zenodo archive (DOI) and the GitHub repository:
https://github.com/AnikPureMath25/Rindler-Blackhole-Model
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