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Logarithmic conformal field theories are based on vertex algebras with non-semisimple representation categories. While examples of such theories have been known for more than 25 years, some crucial aspects of local logarithmic CFTs have…

High Energy Physics - Theory · Physics 2021-07-28 Jürgen Fuchs , Christoph Schweigert

The structure of integrable field theories in the presence of defects is discussed in terms of boundary functions under the Lagrangian formalism. Explicit examples of bosonic and fermionic theories are considered. In particular, the…

High Energy Physics - Theory · Physics 2008-11-26 J. F. Gomes , L. H. Ymai , A. H. Zimerman

The main result of this paper is the construction of a conformally covariant operator in two dimensions acting on scalar fields and containing fourth order derivatives. In this way it is possible to derive a class of Lagrangians invariant…

High Energy Physics - Theory · Physics 2009-10-28 Franco Ferrari

These pedagogical lectures present some material, classical or more recent, on (Rational) Conformal Field Theories and their general setting ``in the bulk'' or in the presence of a boundary. Two well posed problems are the classification of…

High Energy Physics - Theory · Physics 2007-05-23 Jean-Bernard Zuber

Conformal Field Theories (CFTs) are special classes of quantum field theories that find applications ranging from critical phenomena to theories of quantum gravity via holography. Understanding thermal effects in CFTs is crucial:…

High Energy Physics - Theory · Physics 2025-08-05 Alessio Miscioscia

This paper is designed to be a practical tool for constructing and investigating two-point correlation functions in defect conformal field theory, directly in physical space, between any two bulk primaries or between a bulk primary and a…

High Energy Physics - Theory · Physics 2021-05-12 Christopher P. Herzog , Abhay Shrestha

Stochastic Loewner evolutions (SLE) are random growth processes of sets, called hulls, embedded in the two dimensional upper half plane. We elaborate and develop a relation between SLE evolutions and conformal field theories (CFT) which is…

High Energy Physics - Theory · Physics 2008-11-26 Michel Bauer , Denis Bernard

The presence of a boundary (or defect) in a conformal field theory allows one to generalize the notion of an exactly marginal deformation. Without a boundary, one must find an operator of protected scaling dimension $\Delta$ equal to the…

High Energy Physics - Theory · Physics 2020-02-19 Christopher P. Herzog , Itamar Shamir

In this paper we study the constraints imposed by conformal invariance on extended objects a.k.a defects in a conformal field theory. We identify a particularly nice class of defects that is closed under conformal transformations.…

High Energy Physics - Theory · Physics 2016-02-23 Abhijit Gadde

An approach to field theory is studied in which fields are comprised of $N$ constituent random neurons. Gaussian theories arise in the infinite-$N$ limit when neurons are independently distributed, via the Central Limit Theorem, while…

High Energy Physics - Theory · Physics 2021-12-10 James Halverson

We propose a nonperturbative completion of two-point correlators in $T\bar{T}$-deformed conformal field theories (CFTs), and analyze their behavior at distance scales shorter than the fundamental length scale set by the $T\bar{T}$…

High Energy Physics - Theory · Physics 2025-07-28 Shinji Hirano , Vinayak Raj

The conformal field theory on a Z_N-surface is studied by mapping it on the branched sphere. Using a coulomb gas formalism we construct the minimal models of the theory.

High Energy Physics - Theory · Physics 2014-11-18 Samwel Apikyan , Costas Efthimiou

Physicists dating back to Feynman have lamented the difficulties of applying the variational principle to quantum field theories. In non-relativistic quantum field theories, the challenge is to parameterize and optimize over the infinitely…

Quantum Physics · Physics 2024-09-04 John M. Martyn , Khadijeh Najafi , Di Luo

Artificial Neural Networks (ANNs) often represent conflicts between features, arising naturally during training as the network learns to integrate diverse and potentially disagreeing inputs to better predict the target variable. Despite…

Machine Learning · Computer Science 2025-06-03 Adam Dejl , Dekai Zhang , Hamed Ayoobi , Matthew Williams , Francesca Toni

We study one and two point functions of conformal field theories on spaces of maximal symmetry with and without boundaries and investigate their spectral representations. Integral transforms are found, relating the spectral decomposition to…

High Energy Physics - Theory · Physics 2015-09-30 Kurt Hinterbichler , James Stokes , Mark Trodden

We present an analytic study of conformal field theories on the real projective space $\mathbb{RP}^d$, focusing on the two-point functions of scalar operators. Due to the partially broken conformal symmetry, these are non-trivial functions…

High Energy Physics - Theory · Physics 2021-08-11 Simone Giombi , Himanshu Khanchandani , Xinan Zhou

It is a central challenge in deep learning to understand how neural networks learn representations. A leading approach is the Neural Feature Ansatz (NFA) (Radhakrishnan et al. 2024), a conjectured mechanism for how feature learning occurs.…

Machine Learning · Computer Science 2025-09-08 Enric Boix-Adsera , Neil Mallinar , James B. Simon , Mikhail Belkin

We formulate a correspondence between non-relativistic conformal field theories (NRCFTs) in d-1 spatial dimensions and gravitational theories in AdS_{d+2} backgrounds with one compactified lightlike direction. The breaking of the maximal…

High Energy Physics - Theory · Physics 2011-07-05 Walter D. Goldberger

In a four-dimensional space, I shall construct all of the conformally invariant scalar-tensor field theories, which are flat space compatible; i.e., well-defined and differentiable when evaluated for a flat metric tensor and constant scalar…

General Relativity and Quantum Cosmology · Physics 2017-06-16 Gregory W. Horndeski

This paper introduces a scale-invariant methodology employing \textit{Fractal Geometry} to analyze and explain the nonlinear dynamics of complex connectionist systems. By leveraging architectural self-similarity in Deep Neural Networks…

Neural and Evolutionary Computing · Computer Science 2024-07-16 Ambarish Moharil , Damian Tamburri , Indika Kumara , Willem-Jan Van Den Heuvel , Alireza Azarfar