Stored diagnostic runs
Simulation Results
Pareto fronts and scheme-comparison diagnostics from 5 stored result files.
These are small local diagnostics, not physical validation. Fronts come from pymoo NSGA-II; the files' "globalmoo" slots ran local fallbacks (grid search or differential evolution) because GlobalMOO was not connected.
Understanding Renormalization
WHAT: Renormalization is how quantum field theory turns divergent intermediate results into finite predictions. Different schemes (MS-bar, on-shell, MOM) organize the intermediate steps differently but must agree on physical quantities such as pole masses.
HOW: A toy one-loop model is optimized with pymoo NSGA-II over counterterms and the scale mu. One objective is an invariance penalty that measures how much the schemes disagree.
RESULTS: The plot shows the stored Pareto front: scale mu against the invariance penalty, coloured by the stability objective.
LIMITS: Scheme independence of physical predictions is a known property of renormalized QFT, built into this toy model. A small penalty here checks the implementation; it does not test the theory.
QFT Renormalization Scheme Optimization
pymoo NSGA-II over MS-bar, On-shell, MOM. Pareto front: renormalization scale versus scheme invariance penalty.
Source: results/renorm_scheme_results.json (simulations/renormalization_scheme_invariance.py)
Summary: Local Diagnostic Outcomes
5 stored result files across 5 domains. Fronts are pymoo NSGA-II. The comparison slot labelled "globalmoo" in each file ran a local fallback: