Javascript must be enabled to continue!
Quantum field theory (QFT) at finite temperature: Equilibrium properties
View through CrossRef
Abstract
Some equilibrium properties in statistical quantum field theory (QFT), that is, relativistic QFT at finite temperature are reviewed. Study of QFT at finite temperature is motivated by cosmological problems, high energy heavy ion collisions, and speculations about possible phase transitions, also searched for in numerical simulations. In particular, the situation of finite temperature phase transitions, or the limit of high temperature (an ultra-relativistic limit where the temperature is much larger than the physical masses of particles) are discussed. The concept of dimensional reduction emerges, in many cases, statistical properties of finite-temperature QFT in (1, d − 1) dimensions can be described by an effective classical statistical field theory in (d − 1) dimensions. Dimensional reduction generalizes a property already observed in the non-relativistic example of the Bose gas, and indicates that quantum effects are less important at high temperature. The corresponding technical tools are a mode-expansion of fields in the Euclidean time variable, singling out the zero modes of boson fields, followed by a local expansion of the resulting (d − 1)-dimensional effective field theory (EFT). Additional physical intuition about QFT at finite temperature in (1, d−1) dimensions can be gained by considering it as a classical statistical field theory in d dimensions, with finite size in one dimension. This identification makes an analysis of finite temperature QFT in terms of the renormalization group (RG), and the theory of finite-size effects of the classical theory, possible. These ideas are illustrated with several simple examples, the φ4 field theory, the non-linear σ-model, the Gross–Neveu model and some gauge theories.
Title: Quantum field theory (QFT) at finite temperature: Equilibrium properties
Description:
Abstract
Some equilibrium properties in statistical quantum field theory (QFT), that is, relativistic QFT at finite temperature are reviewed.
Study of QFT at finite temperature is motivated by cosmological problems, high energy heavy ion collisions, and speculations about possible phase transitions, also searched for in numerical simulations.
In particular, the situation of finite temperature phase transitions, or the limit of high temperature (an ultra-relativistic limit where the temperature is much larger than the physical masses of particles) are discussed.
The concept of dimensional reduction emerges, in many cases, statistical properties of finite-temperature QFT in (1, d − 1) dimensions can be described by an effective classical statistical field theory in (d − 1) dimensions.
Dimensional reduction generalizes a property already observed in the non-relativistic example of the Bose gas, and indicates that quantum effects are less important at high temperature.
The corresponding technical tools are a mode-expansion of fields in the Euclidean time variable, singling out the zero modes of boson fields, followed by a local expansion of the resulting (d − 1)-dimensional effective field theory (EFT).
Additional physical intuition about QFT at finite temperature in (1, d−1) dimensions can be gained by considering it as a classical statistical field theory in d dimensions, with finite size in one dimension.
This identification makes an analysis of finite temperature QFT in terms of the renormalization group (RG), and the theory of finite-size effects of the classical theory, possible.
These ideas are illustrated with several simple examples, the φ4 field theory, the non-linear σ-model, the Gross–Neveu model and some gauge theories.
Related Results
Minimal immune cell subset differences in a cohort of close contacts of tuberculosis index cases
Minimal immune cell subset differences in a cohort of close contacts of tuberculosis index cases
ABSTRACT
Understanding the perturbations in immune response across the spectrum of TB infection is still unclear. In this study, we followed a cohort of close conta...
Advanced frameworks for fraud detection leveraging quantum machine learning and data science in fintech ecosystems
Advanced frameworks for fraud detection leveraging quantum machine learning and data science in fintech ecosystems
The rapid expansion of the fintech sector has brought with it an increasing demand for robust and sophisticated fraud detection systems capable of managing large volumes of financi...
Quantum Computing and Quantum Information Science
Quantum Computing and Quantum Information Science
Abstract:
Quantum Computing and Quantum Information Science offers a comprehensive, interdisciplinary exploration of the mathematical principles, computational models, and engineer...
Quantum information outside quantum information
Quantum information outside quantum information
Quantum theory, as counter-intuitive as a theory can get, has turned out to make predictions of the physical world that match observations so precisely that it has been described a...
Advancements in Quantum Computing and Information Science
Advancements in Quantum Computing and Information Science
Abstract: The chapter "Advancements in Quantum Computing and Information Science" explores the fundamental principles, historical development, and modern applications of quantum co...
Integrating quantum neural networks with machine learning algorithms for optimizing healthcare diagnostics and treatment outcomes
Integrating quantum neural networks with machine learning algorithms for optimizing healthcare diagnostics and treatment outcomes
The rapid advancements in artificial intelligence (AI) and quantum computing have catalyzed an unprecedented shift in the methodologies utilized for healthcare diagnostics and trea...
Quantum Field Theory and Critical Phenomena
Quantum Field Theory and Critical Phenomena
Abstract
Introduced as a quantum extension of Maxwell's classical theory, quantum electrodynamic (QED) has been the first example of a quantum field theory (QFT). Ev...
Revolutionizing multimodal healthcare diagnosis, treatment pathways, and prognostic analytics through quantum neural networks
Revolutionizing multimodal healthcare diagnosis, treatment pathways, and prognostic analytics through quantum neural networks
The advent of quantum computing has introduced significant potential to revolutionize healthcare through quantum neural networks (QNNs), offering unprecedented capabilities in proc...

