Signal detection in nearly continuous spectra and symmetry breaking
| Autor: | Lahoche, Vincent, Samary, Dine Ousmane, Tamaazousti, Mohamed |
|---|---|
| Přispěvatelé: | Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
High Energy Physics - Theory
principal component analysis [PHYS.HTHE]Physics [physics]/High Energy Physics - Theory [hep-th] FOS: Physical sciences critical phenomena field theory symmetry breaking embedding High Energy Physics - Theory (hep-th) signal detection phase transition big data covariance correlation flow non-Gaussianity potential: local potential: approximation renormalization group: nonperturbative Renormalization group |
| Popis: | The large scale behavior of systems having a large number of interacting degrees of freedom is suitably described using renormalization group, from non-Gaussian distributions. Renormalization group techniques used in physics are then expected to be helpful for issues when standard methods in data analysis break down. Signal detection and recognition for covariance matrices having nearly continuous spectra is currently an open issue in data science and machine learning. Using the field theoretical embedding introduced in arXiv:2011.02376 to reproduces experimental correlations, we show in this paper that the presence of a signal may be characterized by a phase transition with $\mathbb{Z}_2$-symmetry breaking. For our investigations, we use the nonperturbative renormalization group formalism, using a local potential approximation to construct an approximate solution of the flow. Moreover, we focus on the nearly continuous signal build as a perturbation of the Marchenko-Pastur law with many discrete spikes. Comment: 07 pages, 6 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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