Pre-equilibrium de-excitation of superheavy nuclei via muonic internal conversion

This repository contains the computational models, numerical datasets, high-resolution figures, and preprint manuscript associated with the study of Muonic Internal Conversion (MIC) as a pre-equilibrium de-excitation channel for superheavy element (SHE) synthesis. The core computational tool provided here is MuonIC, a phenomenological relativistic Dirac-Coulomb solver. It is designed to evaluate muonic bound states, macroscopic Isoscalar Giant Monopole Resonance (ISGMR) transition widths, and thermodynamic survival probability gains across the 8th period of the periodic table (Z=112 to Z=160). This repository ensures full transparency and reproducibility of the theoretical framework and the proposed 1-exawatt all-optical experimental architecture. File Inventory MuonIC. py: The primary Python physics engine. It handles finite-nuclear-size Coulomb potentials, QED corrections (Vacuum Polarization and Self-Energy), Dirac continuum solving, and the transition/survival logic, comparing the static barrier-enhancement pathway against the dynamic 1n MIC ejection pathway. MuonICᵣesults. csv: The complete, tabulated numerical dataset generated by the global thermodynamic sweep from Z=112 to Z=160. Includes centroid energies, muonic binding depths, partial widths, and absolute evaporation residue (EVR) gains. MICₚreprintZenodo₀8. pdf: The current preprint/manuscript detailing the theoretical formalism, mathematical derivations, and experimental feasibility constraints. Fig1EᵥsZ. pdf: High-resolution plot demonstrating the scaling of muonic binding energy vs. ISGMR centroid energy across the superheavy regime. Fig2PartialWidthᵥsZ. pdf: High-resolution plot mapping the sub-threshold coupling limits and the penalty on the dynamic transition rate. Fig3MICₐllₒpticalₐrchitecture. pdf: Schematic representation of the proposed exawatt-driven, target-synchronized empirical setup required to bypass standard muonic flux constraints. Usage and Reproduction To reproduce the numerical dataset (MuonICᵣesults. csv), execute the Python script in any standard environment. Dependencies: numpy, scipy, pandas, tqdm pip install numpy scipy pandas tqdm python MuonIC. py The script will output the full pathway segmentation to the console and automatically save/overwrite the CSV file in the working directory. License The code and data in this repository are published under the Creative Commons Attribution 4. 0 International license. They are free to use, modify, and distribute for academic and research purposes, given you cite this work. Citation If you utilize this code or the derived theoretical framework in your research, please cite both the manuscript and this software repository. Code snippet @miscrampair2026muonic, author = {Rampair, A. , title = MuonIC: Relativistic Dirac-Coulomb Solver and MIC Thermodynamic Evaluator, month = Apr, year = 2026, publisher = Zenodo, doi = 10. 5281/zenodo. 18463761, url = https: //doi. org/10. 5281/zenodo. 18463761 } Keywords: Superheavy nuclei, Heavy-ion Fusion, Giant Resonances, Survival Probability, Muon-Catalyzed Fusion, Superheavy Synthesis, Exotic Atoms, Muonic Atoms, Island of Stability, Nuclear Equation of State (EoS), BSM Physics, Element 119, Element 120, Evaporation Residue Cross-Secton (EVR).