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This paper presents a rigorous mathematical framework for classifying long-range correlations in quantum field theory using conservation laws and coarse-graining techniques, specifically targeting the emergence of spectral poles from ladder diagrams. By distinguishing between operators protected by Ward or BRST symmetries versus unprotected ones, it establishes definitive criteria for determining which physical channels can exhibit critical behavior without relying on nonperturbative computation at that stage. The work advances fundamental theoretical understanding in quantum field theory and statistical physics rather than addressing practical AI applications or quantum computing hardware implementations.
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Coarse-Graining and the Classification of Long-Range Correlations in Quantum Field Theory

arXiv3 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2608.01989v1 Announce Type: cross Abstract: A conservation-law-guided classification framework is developed for the execution of coarse-graining operations in quantum field theory. Coarse-graining produces a selection at the level of Feynman diagrams. Diagrams with nonzero momentum transfer are suppressed by oscillatory factors, zero-momentum-transfer ladder diagrams can accumulate through geometric series resummation to produce a spectral pole, and single-bubble topologies contribute only to the continuum. Conservation laws govern this classification. For operators protected by a Ward identity, the matrix element at zero momentum is guaranteed to be nonvanishing. For operators protected by BRST symmetry, the Slavnov-Taylor identities provide no mandatory suppression. The two cases differ in the strength of the algebraic guarantee. For unprotected operators the injection term vanishes and the spectral function remains continuous. The sign of the single-bubble contribution is determined by spin statistics. A positive sign leads to amplificative feedback in the ladder resummation, a negative sign leads to suppressive feedback. These two attributes, the nonvanishing of the injection term and the sign of the single-bubble contribution, are the defining criteria of the classification. As a direct application, a general classification of local operators is established within the emergence framework and verified on eight physical channels and three known solvable systems. The framework indicates which emergence paths are possible for each operator; whether the critical condition is reached is left for independent nonperturbative computation. The logical structure of this classification is parallel to the strategy used in deriving fluid equations from molecular kinetic theory in classical statistical physics.

Abstract:A conservation-law-guided classification framework is developed for the execution of coarse-graining operations in quantum field theory. Coarse-graining produces a selection at the level of Feynman diagrams. Diagrams with nonzero momentum transfer are suppressed by oscillatory factors, zero-momentum-transfer ladder diagrams can accumulate through geometric series resummation to produce a spectral pole, and single-bubble topologies contribute only to the continuum. Conservation laws govern this classification. For operators protected by a Ward identity, the matrix element at zero momentum is guaranteed to be nonvanishing. For operators protected by BRST symmetry, the Slavnov-Taylor identities provide no mandatory suppression. The two cases differ in the strength of the algebraic guarantee. For unprotected operators the injection term vanishes and the spectral function remains continuous. The sign of the single-bubble contribution is determined by spin statistics. A positive sign leads to amplificative feedback in the ladder resummation, a negative sign leads to suppressive feedback. These two attributes, the nonvanishing of the injection term and the sign of the single-bubble contribution, are the defining criteria of the classification. As a direct application, a general classification of local operators is established within the emergence framework and verified on eight physical channels and three known solvable systems. The framework indicates which emergence paths are possible for each operator; whether the critical condition is reached is left for independent nonperturbative computation. The logical structure of this classification is parallel to the strategy used in deriving fluid equations from molecular kinetic theory in classical statistical physics.

33pages, 3 tables, no figures. The CGC source code is included as ancillary files and is also available at this https URL

arXiv:2608.01989 [hep-th]

(or arXiv:2608.01989v1 [hep-th] for this version)

https://doi.org/10.48550/arXiv.2608.01989 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Conservation-Guided_Classification/README.md

Conservation-Guided_Classification/cgc/__init__.py

Conservation-Guided_Classification/cgc/channels/__init__.py

Conservation-Guided_Classification/cgc/channels/fermion_bilinears.py

Conservation-Guided_Classification/cgc/channels/gauge_field.py

Conservation-Guided_Classification/cgc/channels/higgs_quartic.py

Conservation-Guided_Classification/cgc/channels/tmunu_spin2.py

Conservation-Guided_Classification/cgc/engine/__init__.py

Conservation-Guided_Classification/cgc/engine/conservation_checker.py

Conservation-Guided_Classification/cgc/engine/diagram_builder.py

Conservation-Guided_Classification/cgc/engine/diagram_generator.py

Conservation-Guided_Classification/cgc/engine/fa_model_parser.py

Conservation-Guided_Classification/cgc/engine/feynarts_backend.py

Conservation-Guided_Classification/cgc/engine/momentum_classifier.py

Conservation-Guided_Classification/cgc/engine/multi_loop_generator.py

Conservation-Guided_Classification/cgc/engine/one_loop_generator.py

Conservation-Guided_Classification/cgc/engine/pi0_flat_continuum.py

Conservation-Guided_Classification/cgc/engine/pipeline.py

Conservation-Guided_Classification/cgc/engine/qgraf_backend.py

Conservation-Guided_Classification/cgc/engine/resummation.py

Conservation-Guided_Classification/cgc/engine/topology_classifier.py

Conservation-Guided_Classification/cgc/engine/two_loop_topologies.py

Conservation-Guided_Classification/cgc/params.py

Conservation-Guided_Classification/cgc/requirements.txt

Conservation-Guided_Classification/pyproject.toml

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