\begin{table}%t2 %\centering \par \caption {\label{tab:24chart}Differential number counts at 24~$\mu$m. } %\centerline {\begin{tabular}{rrrrrrrr} \hline\hline \noalign{\smallskip} $\langle S\rangle$ & $S_{\rm min}$ & $S_{\rm max}$ & d$N/{\rm d}S.S^{2.5}$ & $\sigma_{\rm poisson}$ & $\sigma_{\rm clustering}$ & $\sigma_{\rm clus.+calib.}$ & $\Omega_{\rm used}$\\ \hline \multicolumn{3}{c}{(in mJy)} & \multicolumn{4}{c}{(in gal~Jy$^{1.5}$~sr$^{-1}$)} & deg$^2$\\ 0.040 & 0.035 & 0.044 & 17.5 & 1.0 & 1.1 & 1.3 & 0.2 \\ 0.050 & 0.044 & 0.056 & 21.4 & 1.0 & 1.1 & 1.4 & 0.2 \\ 0.064 & 0.056 & 0.071 & 28.2 & 1.2 & 1.5 & 1.8 & 0.2 \\ 0.081 & 0.071 & 0.090 & 36.2 & 1.5 & 1.9 & 2.4 & 0.2 \\ 0.102 & 0.090 & 0.114 & 52.6 & 1.3 & 1.9 & 2.9 & 0.6 \\ 0.130 & 0.114 & 0.145 & 64.1 & 1.0 & 1.7 & 3.1 & 3.4 \\ 0.164 & 0.145 & 0.184 & 78.7 & 1.1 & 2.2 & 3.8 & 3.4 \\ 0.208 & 0.184 & 0.233 & 89.8 & 1.3 & 2.8 & 4.5 & 3.4 \\ 0.264 & 0.233 & 0.295 & 96.5 & 1.5 & 3.3 & 5.1 & 3.4 \\ 0.335 & 0.295 & 0.374 & 112.0 & 0.8 & 1.8 & 4.8 & 37.2 \\ 0.424 & 0.374 & 0.474 & 103.7 & 0.6 & 1.7 & 4.5 & 46.1 \\ 0.538 & 0.474 & 0.601 & 91.9 & 0.6 & 1.5 & 4.0 & 53.6 \\ 0.681 & 0.601 & 0.762 & 81.2 & 0.6 & 1.5 & 3.6 & 53.6 \\ 0.863 & 0.762 & 0.965 & 72.8 & 0.7 & 1.6 & 3.3 & 53.6 \\ 1.094 & 0.965 & 1.223 & 65.3 & 0.8 & 1.6 & 3.1 & 53.6 \\ 1.387 & 1.223 & 1.550 & 60.8 & 0.9 & 1.7 & 3.0 & 53.6 \\ 1.758 & 1.550 & 1.965 & 56.7 & 1.0 & 1.8 & 2.9 & 53.6 \\ 2.228 & 1.965 & 2.490 & 55.4 & 1.2 & 2.1 & 3.0 & 53.6 \\ 2.823 & 2.490 & 3.156 & 54.0 & 1.5 & 2.3 & 3.2 & 53.6 \\ 3.578 & 3.156 & 4.000 & 55.9 & 1.8 & 2.7 & 3.5 & 53.6 \\ 5.807 & 4.000 & 7.615 & 54.8 & 1.5 & 2.9 & 3.6 & 53.6 \\ 11.055 & 7.615 & 14.496 & 46.9 & 2.3 & 3.6 & 4.1 & 53.6 \\ 21.045 & 14.496 & 27.595 & 36.4 & 3.3 & 4.4 & 4.6 & 53.6 \\ 40.063 & 27.595 & 52.531 & 43.4 & 5.9 & 7.7 & 7.9 & 53.6 \\ 76.265 & 52.531 & 100.000 & 47.7 & 9.9 & 12.0 & 12.2 & 53.6 \\ \hline \end{tabular}} \tablefoot {$\sigma_{\rm clustering}$ is the uncertainty taking into account clustering (see Sect.~\ref{subsection:sectclus}). $\sigma_{\rm clus.+calib.}$ takes into account both clustering and calibration \citep{Engelbracht2007}.} \end{table}