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Renormalization group techniques are widely used in modern physics to describe the low energy relevant aspects of systems involving a large number of degrees of freedom. Those techniques are thus expected to be a powerful tool to address…

High Energy Physics - Theory · Physics 2021-11-19 Vincent Lahoche , Dine Ousmane Samary , Mohamed Tamaazousti

Signal detection is one of the main challenges of data science. As it often happens in data analysis, the signal in the data may be corrupted by noise. There is a wide range of techniques aimed at extracting the relevant degrees of freedom…

High Energy Physics - Theory · Physics 2024-08-05 Harold Erbin , Riccardo Finotello , Bio Wahabou Kpera , Vincent Lahoche , Dine Ousmane Samary

This review paper uses renormalization group techniques for signal detection in nearly-continuous positive spectra. We highlight universal aspects of the analogue field-theory approach. The first aim is to present an extended…

High Energy Physics - Theory · Physics 2025-11-27 Vincent Lahoche , Dine Ousmane Samary , Mohamed Tamaazousti

The tensorial principal component analysis is a generalization of ordinary principal component analysis, focusing on data which are suitably described by tensors rather than matrices. This paper aims at giving the nonperturbative…

High Energy Physics - Theory · Physics 2021-11-04 Vincent Lahoche , Mohamed Ouerfelli , Dine Ousmane Samary , Mohamed Tamaazousti

We establish a correspondence between anomaly detection in high-noise regimes and the renormalization group flow of non-equilibrium field theories. We provide a physical grounding for this framework by proving that the detection of phase…

Statistical Mechanics · Physics 2026-05-25 Riccardo Finotello , Vincent Lahoche , Parham Radpay , Dine Ousmane Samary

Consider a network consisting of two subnetworks (communities) connected by some external edges. Given the network topology, the community detection problem can be cast as a graph partitioning problem that aims to identify the external…

Social and Information Networks · Computer Science 2023-07-19 Pin-Yu Chen , Alfred O. Hero

Signal detection in high dimensions is a critical challenge in data science. While standard methods based on random matrix theory provide sharp detection thresholds for finite-rank perturbations, such as the known Baik-Ben Arous-P\'ech\'e…

Data Analysis, Statistics and Probability · Physics 2026-05-11 Riccardo Finotello , Vincent Lahoche , Dine Ousmane Samary

If supersymmetric particles are discovered, an important problem will be to determine how supersymmetry has been broken. At collider energies, supersymmetry breaking can be parameterised by soft supersymmetry breaking parameters. Several…

High Energy Physics - Phenomenology · Physics 2012-11-07 Jamil Hetzel

In the area of topological and geometric treatment of phase transitions and symmetry breaking in Hamiltonian systems, in a recent paper some general sufficient conditions for these phenomena in $\mathbb{Z}_2$-symmetric systems (i.e.…

Statistical Mechanics · Physics 2016-11-07 Fabrizio Baroni

Some recent results showed that renormalization group can be considered as a promising framework to address open issues in data analysis. In this work, we focus on one of these aspects, closely related to principal component analysis for…

High Energy Physics - Theory · Physics 2022-03-04 Vincent Lahoche , Dine Ousmane Samary , Mohamed Tamaazousti

The spontaneous breaking of a $Z_2$ symmetry typically gives rise to emergent excitations possessing the same symmetry with a renormalized mass. Contrary to this conventional wisdom, we present a theory in which the low-lying excitation in…

Quantum Physics · Physics 2025-10-14 Yue Yu , Myung-Joong Hwang

We present a physical interpretation of machine learning functions, opening up the possibility to control properties of statistical systems via the inclusion of these functions in Hamiltonians. In particular, we include the predictive…

High Energy Physics - Lattice · Physics 2021-02-17 Dimitrios Bachtis , Gert Aarts , Biagio Lucini

The collective mode spectrum of a symmetry-breaking state, such as a superconductor, provides crucial insight into the nature of the order parameter. In this context, we present a microscopic weak-coupling theory for the collective modes of…

Superconductivity · Physics 2022-04-12 Nicholas R. Poniatowski , Jonathan B. Curtis , Amir Yacoby , Prineha Narang

We consider a group synchronization problem with multiple frequencies which involves observing pairwise relative measurements of group elements on multiple frequency channels, corrupted by Gaussian noise. We study the computational phase…

Statistics Theory · Mathematics 2024-06-06 Anastasia Kireeva , Afonso S. Bandeira , Dmitriy Kunisky

Financial stock return correlations have been analyzed through the lens of random matrix theory to differentiate the underlying signal from spurious correlations. The continuous spectrum of the eigenvalue distribution derived from the stock…

Statistical Finance · Quantitative Finance 2025-11-11 Ixandra Achitouv , Vincent Lahoche , Dine Ousmane Samary

Anomalous global symmetries, which can be realized on the boundary of symmetry-protected topological phases, brings new phases and phase transitions to condensed matter physics. In this work, we study a one dimensional model with an…

Strongly Correlated Electrons · Physics 2022-11-11 Jin-Xiang Hao , Wei Li , Yang Qi

We demonstrate a novel feature of certain phase transitions in theories with large rank symmetry group that exhibit specific types of non-local interactions. A typical example of such a theory is a large-$N$ gauge theory where by `non-local…

High Energy Physics - Theory · Physics 2020-11-18 Masanori Hanada , Brandon Robinson

We consider the dynamics of bodies with "active" microstructure described by vector-valued phase fields. For waves with time-varying amplitude, the associated evolution equation involves a matrix that can be non-normal, depending on the…

Mathematical Physics · Physics 2025-02-18 Michele Benzi , Daniele La Pegna , Paolo Maria Mariano

In this paper, we study the phase transition behavior emerging from the interactions among multiple agents in the presence of noise. We propose a simple discrete-time model in which a group of non-mobile agents form either a fixed connected…

Optimization and Control · Mathematics 2008-10-21 Jialing Liu , Vikas Yadav , Hullas Sehgal , Joshua M. Olson , Haifeng Liu , Nicola Elia

The functional renormalization group (FRG) has been used widely to investigate phase diagrams, in particular the one of the two-dimensional Hubbard model. So far, the study of one-dimensional models has not attracted as much attention. We…

Strongly Correlated Electrons · Physics 2018-06-18 Lisa Markhof , Björn Sbierski , Volker Meden , Christoph Karrasch
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