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Grassmannian stochastic analysis and the stochastic quantization of Euclidean Fermions

Sergio Albeverio, Luigi Borasi, Francesco C. De Vecchi and Massimiliano Gubinelli

First issued 20 April 2020 · PDF · HTML

Abstract

We introduce a stochastic analysis of Grassmann random variables suitable for the stochastic quantization of Euclidean fermionic quantum field theories. Analysis on Grassmann algebras is developed here from the point of view of quantum probability: a Grassmann random variable is an homomorphism of an abstract Grassmann algebra into a quantum probability space, i.e. a $C^{\ast}$-algebra endowed with a suitable state. We define the notion of Gaussian processes, Brownian motion and stochastic (partial) differential equations taking values in Grassmann algebras. We use them to study the long time behavior of finite and infinite dimensional Langevin Grassmann stochastic differential equations driven by Gaussian space-time white noise and to describe their invariant measures. As an application we give a proof of the stochastic quantization and of the removal of the space cut-off for the Euclidean Yukawa model.

Versions

  1. Version 4 (arXiv v4) current
    1 February 2022
    arXiv comments: 72 pages, the introduction and Section 5 have been revised, former Appendix A and Appendix E have been removed, some typos and other minor errors have been corrected, some references have been added
  2. Version 3 (arXiv v3)
    21 January 2021
    arXiv comments: 71 pages, some typos and the proof of Lemma 68 have been corrected, some references and one new appendix have been added
  3. Version 2 (arXiv v2)
    25 May 2020
    arXiv comments: 84 pages, typos corrected, added literature and two new appendices
  4. Version 1 (arXiv v1)
    20 April 2020

Cite

Sergio Albeverio, Luigi Borasi, Francesco C. De Vecchi and Massimiliano Gubinelli. Grassmannian stochastic analysis and the stochastic quantization of Euclidean Fermions. Papers of Massimiliano Gubinelli, 2020, version 4. https://mgubi.org/papers/paper/grassmann-stochastic-analysis/

Licence: arXiv.org perpetual, non-exclusive license to distribute; all other rights reserved to the authors

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Papers of Massimiliano Gubinelli: an OpenSheaf archive (format 0.1, a draft); this page is generated by opensheaf-py from the archive.