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• W3105369529 startingPage "083005" @default.
• W3105369529 abstract "In physics, one attempts to infer the rules governing a system given only the results of imperfect measurements. Hence, microscopic theories may be effectively indistinguishable experimentally. We develop an operationally motivated procedure to identify the corresponding equivalence classes of states, and argue that the renormalization group (RG) arises from the inherent ambiguities associated with the classes: one encounters flow parameters as, e.g., a regulator, a scale, or a measure of precision, which specify representatives in a given equivalence class. This provides a unifying framework and reveals the role played by information in renormalization. We validate this idea by showing that it justifies the use of low-momenta n-point functions as statistically relevant observables around a Gaussian hypothesis. These results enable the calculation of distinguishability in quantum field theory. Our methods also provide a way to extend renormalization techniques to effective models which are not based on the usual quantum-field formalism, and elucidates the relationships between various type of RG." @default.
• W3105369529 created "2020-11-23" @default.
• W3105369529 creator A5053046317 @default.
• W3105369529 creator A5074511091 @default.
• W3105369529 date "2015-08-05" @default.
• W3105369529 modified "2023-10-17" @default.
• W3105369529 title "The renormalization group via statistical inference" @default.
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• W3105369529 doi "https://doi.org/10.1088/1367-2630/17/8/083005" @default.
• W3105369529 hasPublicationYear "2015" @default.
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