dominate the fit. We have decided to stick to the units adopted by @OBrien2010D, as this offered us the best way to compare results.

In Paper III, we performed a Monte-Carlo Markov-Chain (MCMC) fit to the neutral hydrogen, such that a set of samples from the so-called chain together cover the likelihood distribution of the parameters. Our fitting strategy here makes use of this likelihood distribution. We take the last 1000 samples from the chain and perform a fit of the dark matter halo to each individual sample. In total, we thus get 1000 solutions for the halo. We make use of the <span style="font-variant:small-caps;">PSwarm</span> particle swarm optimization algorithm [@Vaz09], as implemented through the <span style="font-variant:small-caps;">OpenOpt</span> library [@Kroshko]. The <span style="font-variant:small-caps;">PSwarm</span> is an example of a global optimization routine, which can avoid being stuck in local optima in the solution space. The fit is performed directly to $\chi^2$ as defined in Equation \[eqn:halochi\]. In some cases, the models converge to unrealistic solutions. We therefore base our results on the 25% of the samples with the lowest $\chi^2$ errors. The halo model uses three free parameters: the halo central density $\rho_{0,\textrm{halo}}$, the scale length $R_c$ and the halo shape $q$. Together 