Branching Brownian motion with selection
  In this thesis, branching Brownian motion (BBM) is a random particle system where the particles diffuse on the real line according to Brownian motions and branch at constant rate into a random number of particles with expectation greater than 1. We study two models of BBM with selection: BBM with absorption at a space-time line and the N-BBM, where, as soon as the number of particles exceeds a given number N, only the N right-most particles are kept, the others being removed from the system. For the first model, we study the law of the number of absorbed particles in the case where the process gets extinct almost surely, using a relation between the Fisher-Kolmogorov-Petrovskii-Piskounov (FKPP) and the Briot-Bouquet equations. For the second model, the study of which represents the biggest part of the thesis, we give a precise asymptotic on the position of the cloud of particles when N is large. More precisely, we show that it converges at the timescale log^3 N to a L\'evy process plus a linear drift, both of them explicit, which confirms a prediction by Brunet, Derrida, Mueller and Munier. This study contributes to the understanding of travelling waves of FKPP type under the influence of noise. Finally, in a third part we point at the relation between the BBM and stable point processes. 
Pascal Maillard
math.PR math-ph math.MP