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Table 4:

Differential number counts at 160 $\mu $m. $\sigma _{clustering}$ is the uncertainty taking into account clustering (see Sect. 4.5).

$\langle S\rangle$
$S_{\rm min}$ $S_{\rm max}$ d $N/{\rm d}S.S^{2.5}$ $\sigma_{\rm poisson}$ $\sigma_{\rm clustering}$ $\sigma_{\rm clus.+calib.}$ $\Omega_{\rm used}$
(in mJy) (in gal Jy1.5 sr-1) deg2
45.747 40.000 51.493 16855. 1312. 2879. 3519. 3.0
58.891 51.493 66.289 14926. 1243. 2704. 3243. 3.0
75.813 66.289 85.336 13498. 1319. 2648. 3104. 3.0
97.596 85.336 109.860 12000. 1407. 2442. 2835. 3.0
125.640 109.860 141.420 10687. 457. 991. 1621. 36.2
161.740 141.420 182.060 7769. 425. 773. 1211. 42.9
208.210 182.060 234.370 7197. 472. 810. 1184. 42.9
268.040 234.370 301.710 5406. 487. 734. 979. 42.9
345.050 301.710 388.400 5397. 585. 843. 1063. 42.9
444.200 388.400 500.000 4759. 662. 891. 1059. 42.9
750.000 500.000 1000.000 6258. 685. 1158. 1380. 42.9
1500.000 1000.000 2000.000 4632. 989. 1379. 1487. 42.9

Notes.  $\sigma_{clus.+calib.}$ takes into account both clustering and calibration (Stansberry et al. 2007).


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