Soutenance de thèse:
Extreme value statistics of strongly correlated systems: fermions, random matrices and random walks
-Djalil Chafaï (CEREMADE, Université Paris-Dauphine)
-Andrea Gambassi (Scuola Internazionale Superiore di Studi Avanzati, Italy)
-Jean-Marc Luck (IPhT, CEA Saclay)
-Satya N. Majumdar (LPTMS, Université Paris-Sud)
-Grégory Schehr (LPTMS, Université Paris-Sud), directeur de thèse
-Christophe Texier (LPTMS, Université Paris-Sud)
-Patrizia Vignolo (INPHYNI, Université de Nice-Sophia Antipolis)
-Pierpaolo Vivo (King’s College London, UK)
Predicting the occurrence of extreme events is a crucial issue in many contexts, ranging from meteorology to finance. For independent and identically distributed (i.i.d.) random variables, three universality classes were identified (Gumbel, Fréchet and Weibull) for the distribution of the maximum. While modelling disordered systems by i.i.d. random variables has been successful with Derrida’s random energy model, this hypothesis fail for many physical systems which display strong correlations. In this thesis, we study three physically relevant models of strongly correlated random variables: trapped fermions, random matrices and random walks.
In the first part, we show several exact mappings between the ground state of a trapped Fermi gas and ensembles of random matrix theory. The Fermi gas is inhomogeneous in the trapping potential and in particular there is a finite edge beyond which its density vanishes. Going beyond standard semi-classical techniques (such as local density approximation), we develop a precise description of the spatial statistics close to the edge. This description holds for a large universality class of hard edge potentials. We apply these results to compute the statistics of the position of the fermion the farthest away from the centre of the trap, the number of fermions in a given domain (full counting statistics) and the related bipartite entanglement entropy. Our analysis also provides solutions to open problems of extreme value statistics in random matrix theory. We obtain for instance a complete description of the fluctuations of the largest eigenvalue in the complex Ginibre ensemble.
In the second part of the thesis, we study extreme value questions for random walks. We consider the gap statistics, which requires to take explicitly into account the discreteness of the process. This question cannot be solved using the convergence of the process to its continuous counterpart, the Brownian motion. We obtain explicit analytical results for the gap statistics of the walk with a Laplace distribution of jumps and provide numerical evidence suggesting the universality of these results.