Super-linear scaling of offsprings at criticality in branching processes
For any branching process, we demonstrate that the typical total number $r_{\rm mp}(ντ)$ of events triggered over all generations within any sufficiently large time window $τ$ exhibits, at criticality, a super-linear dependence $r_{\rm mp}(ντ) \sim (ντ)^γ$ (with $γ>1$) on the total number $ντ$ of the immigrants arriving at the Poisson rate $ν$. In branching processes in which immigrants (or sources) are characterized by fertilities distributed according to an asymptotic power law tail with tail exponent $1 < γ\leqslant 2$, the exponent of the super-linear law for $r_{\rm mp}(ντ)$ is identical to the exponent $γ$ of the distribution of fertilities. For $γ>2$ and for standard branching processes without power law distribution of fertilities, $r_{\rm mp}(ντ) \sim (ντ)^2$. This novel scaling law replaces and tames the divergence $ντ/(1-n)$ of the mean total number ${\bar R}_t(τ)$ of events, as the branching ratio (defined as the average number of triggered events of first generation per source) tends to 1. The derivation uses the formalism of generating probability functions. The corresponding prediction is confirmed by numerical calculations and an heuristic derivation enlightens its underlying mechanism. We also show that ${\bar R}_t(τ)$ is always linear in $ντ$ even at criticality ($n=1$). Our results thus illustrate the fundamental difference between the mean total number, which is controlled by a few extremely rare realizations, and the typical behavior represented by $r_{\rm mp}(ντ)$.