shown to provide an excellent fit to the data. It has 4 free parameters which are summarized in . Note that an important advantage of this model is that it comes with a fully analytical solution to the transport equation. This is particularly attractive in the context of dark matter predictions [@MaurinEtAl2002; @LavalleEtAl2012].

### : the ‘old-fashion’ strong reacceleration model {#sssec:quaint}

Our last benchmark model is the  model, which is also a subpart of the  model, and which aims at describing the low-rigidity features mostly in terms of reacceleration and convection. This model is actually the direct descendant of the [*min*]{}-[*med*]{}-[*max*]{} models [@MaurinEtAl2001; @DonatoEtAl2004] as it relies on almost the same configuration space, except for the high-rigidity break in the diffusion coefficient (which was not observed at the time of its ancestors and will anyway be treated as a nuisance parameter in the statistical analysis). Large $V_{\rm A}$, in combination with a non-trivial value of $\eta\lesssim 0$ is needed to provide decent fits to the data. A large $V_{\rm A}$ in turn couples low-rigidity and high-rigidity features, maximizing parameter correlations. The model has 5 free parameters, made explicit in . In practice, the diffusion coefficient associated with the  model is 