All Figures

$\begin{figure} \par\includegraphics[width=7.8cm,clip]{1964fi01.ps} \end{figure}$ Figure 1: Lay-out of the VIMOS field of view. INVAR masks with laser-cut slits are placed on the focal plane within the four rectangular areas ("VIMOS channels'').
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In the text

$\begin{figure} \par\includegraphics[width=7.3cm,clip]{1964fi02.ps}\hspace*{7mm} \includegraphics[width=7.3cm,clip]{1964fi03.ps} \end{figure}$ Figure 2: Galaxy distribution in a mock VVDS-02h catalog, constructed using the GalICS simulations with the same lay-out as the 20 observed pointings in the actual first-epoch VVDS field and applying the full range of selection effects present in the data, as e.g. the photometric mask. The left panel shows the parent photometric field, including all objects with $I_{AB} \leq 24$ within the current VVDS-02h boundaries and mask. In the right panel only the objects selected for spectroscopy are shown. Note the density gradient towards the central part of the field, due to multiple passes over the same area.
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In the text

$\begin{figure} \par\includegraphics[width=7.5cm,clip]{1964fi04.ps} \end{figure}$ Figure 3: Spectroscopic targets (filled circles) selected in one of the four VIMOS quadrants from a complete VVDS mock photometric sample (open circles). Note how the optimization software tends to select spectroscopic targets aligned along horizontal rows, while, clearly, very close pairs are not observed. Typically, however, 4 independent observations are conducted on the same area, each with a similar target layout, but shifted by a few arcminutes. This significantly reduces both the alignment and proximity effects. The residual bias is then further corrected by the weighting scheme discussed in Sect. 4. Overall, the four passes produce a typical sampling rate of one galaxy in four.
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In the text

$\begin{figure} \par\includegraphics[width=7.7cm,clip]{1964fi05.ps} \end{figure}$ Figure 4: Average redshift distribution in the 50 mock VVDS-02h surveys, normalized by the number of objects in each cone, compared to the redshift distribution of the observed VVDS galaxies. Note how the semi-analytic model of galaxy formation used to construct the GalICS simulations differs from the real data. This is not a concern for the purposes of this work: first, we are performing internal tests of the effect of observing biases and on their correction, which depends on the small-medium scale clustering properties. Second, when error bars are estimated for a specific redshift slice, their amplitude is re-normalized accordingly, to account for the different number of galaxies.
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In the text

$\begin{figure} \par\includegraphics[width=7.5cm,clip]{1964fi06.ps} \end{figure}$ Figure 5: Number counts of artificial stars added to the GalICS simulation, compared to the actual counts of stars in the VVDS-02h field, identified morphologically from the photometric data. The excess in the VVDS above I_AB=20 is due to the inability of the morphological compactness criteria to discriminate stars from galaxies and QSOs at faint magnitudes. When this is taken into account, the models from Robin et al. (2003) reproduce very well the actual distribution of stellar objects in the VVDS.
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In the text

$\begin{figure} \par\includegraphics[width=7.4cm,clip]{1964fi07.ps}\end{figure}$ Figure 6: Spectroscopic success rate per magnitude bin in the VVDS 02h field, including only those redshifts used for the clustering analysis.
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In the text

$\begin{figure} \par\includegraphics[width=7.5cm,clip]{1964fi08.ps} \end{figure}$ Figure 7: Average redshift distribution in the GalICS mock catalogs before and after the full observing strategy is applied. No bias in the redshift distribution is observed.
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In the text

$\begin{figure} \par\includegraphics[width=7.3cm,clip]{1964fi09.ps} \end{figure}$ Figure 8: Impact of the observational process on the estimate of the angular two-point correlation function $\omega (\theta )$ for one mock VVDS survey (open circles), compared to that of the original parent field (filled circles), for one mock VVDS cone. The large distortion, introduced by the observing strategy affects practically all angular scales.
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In the text

$\begin{figure} \par\includegraphics[width=7.6cm,clip]{1964fi10.ps}\hspace*{5mm} \includegraphics[width=7.6cm,clip]{1964fi11.ps} \end{figure}$ Figure 9: Redshift-space two-point correlation function $\xi (s)$ for one mock VVDS-02h field, computed in four redshift bins. The true $\xi (s)$ computed for the whole parent sample (stars) is compared to that measured from the "observed'' sample, first without any correction (open circles, left four panels) and then applying our correction scheme (triangles, right four panels). Error bars are the $1\sigma$ ensemble rms among the 50 VVDS mock samples.
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In the text

$\begin{figure} \par\includegraphics[width=7.6cm,clip]{1964fi12.ps}\hspace*{5mm} \includegraphics[width=7.6cm,clip]{1964fi13.ps} \end{figure}$ Figure 10: Same as Fig. 9, but for the $\xi (r_{\rm p},\pi )$ correlation function. The contours correspond to values for $\xi (r_{\rm p},\pi )$ of 0.4, 1 (bold), 2.0, 5.0. Dashed lines refer to the complete mock sample, while solid ones describe the sample after applying the VVDS selection function.
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In the text

$\begin{figure} \par\includegraphics[width=7.6cm,clip]{1964fi14.ps}\hspace*{5mm} \includegraphics[width=7.6cm,clip]{1964fi15.ps} \end{figure}$ Figure 11: Same as Figs. 9 and 10, but for the projected function $w_{\rm p}(r_{\rm p})$ , measured before (dashed line) and after (solid line) the full observing strategy has been applied. This comparison shows that our method is able to properly recover $w_{\rm p}(r_{\rm p})$ . We note, however, that, being closely related to the angular function, $w_{\rm p}(r_{\rm p})$ remains the most sensitive among the 3D correlation functions to the observational biases and the most difficult to recover properly in all $r_{\rm p}$ bins.
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In the text

$\begin{figure} \includegraphics[width=8.2cm,clip]{1964fi16.ps} \end{figure}$ Figure 12: Evolution of the projected function $w_{\rm p}(r_{\rm p})$ ( left column) and the corresponding best-fit parameters of $\xi (r)$ , r₀ and ${\gamma }$ ( right column), as seen in one of the VVDS mock surveys. Error bars are computed as explained in the text, while error contours on the fit parameters are obtained taking into account the full covariance matrix. The $68.3\%$ , $90\%$ and $95.4\%$ joint confidence levels are defined as in Numerical Recipes (Numerical Recipes, Press et al. 1992, chapter 15.6) in terms of the corresponding likelihood intervals that we obtain from our fitting procedure (see Sect. 5.2).
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In the text

$\begin{figure} \par\includegraphics[width=7.6cm,clip]{1964fi17.ps} \end{figure}$ Figure 13: Evolution of r₀ in a VVDS mock survey (filled circles), compared to that of its parent catalog (open circles). Error bars are as explained in the text. The "true'' and "measured'' values of r₀ are very consistent within the error bars, providing an internal proof of the quality of our correction scheme.
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In the text

$\begin{figure} \par\includegraphics[width=7.7cm,clip]{1964fi18.ps} \end{figure}$ Figure 14: Histograms of the measurements of r₀ and ${\gamma }$ in the redshift bin [0.5-0.7] (chosen as a representative case), among the 50 mock catalogs, for the full cones ( left column) and for the observed samples ( right column), where the full weighting scheme has been applied. The ensemble averaged values of r₀ and ${\gamma }$ are indicated in each panel, together with their rms error.
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In the text

$\begin{figure} \par\includegraphics[width=7.7cm,clip]{1964fi19.ps} \end{figure}$ Figure 15: Measured $w_{\rm p}(r_{\rm p})$ in the case of different number of passes over the same field. When the field is observed only once we are clearly not able to properly recover properly $w_{\rm p}(r_{\rm p})$ on the smallest scale. When we observe the field more times the recovery is much better also on the small scales.
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In the text

$\begin{figure} \par\includegraphics[width=7.5cm,clip]{1964fi20.ps} \end{figure}$ Figure 16: Measurements of $\xi (s)$ for a different number of observing "visits'' over the same field.
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In the text

$\begin{figure} \par\includegraphics[width=7.8cm,clip]{1964fi01.ps} \end{figure}$	Figure 1: Lay-out of the VIMOS field of view. INVAR masks with laser-cut slits are placed on the focal plane within the four rectangular areas ("VIMOS channels'').
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$\begin{figure} \par\includegraphics[width=7.4cm,clip]{1964fi07.ps}\end{figure}$	Figure 6: Spectroscopic success rate per magnitude bin in the VVDS 02h field, including only those redshifts used for the clustering analysis.
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$\begin{figure} \par\includegraphics[width=7.5cm,clip]{1964fi08.ps} \end{figure}$	Figure 7: Average redshift distribution in the GalICS mock catalogs before and after the full observing strategy is applied. No bias in the redshift distribution is observed.
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$\begin{figure} \par\includegraphics[width=7.3cm,clip]{1964fi09.ps} \end{figure}$	Figure 8: Impact of the observational process on the estimate of the angular two-point correlation function $\omega (\theta )$ for one mock VVDS survey (open circles), compared to that of the original parent field (filled circles), for one mock VVDS cone. The large distortion, introduced by the observing strategy affects practically all angular scales.
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$\begin{figure} \par\includegraphics[width=7.6cm,clip]{1964fi12.ps}\hspace*{5mm} \includegraphics[width=7.6cm,clip]{1964fi13.ps} \end{figure}$	Figure 10: Same as Fig. 9, but for the $\xi (r_{\rm p},\pi )$ correlation function. The contours correspond to values for $\xi (r_{\rm p},\pi )$ of 0.4, 1 (bold), 2.0, 5.0. Dashed lines refer to the complete mock sample, while solid ones describe the sample after applying the VVDS selection function.
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$\begin{figure} \par\includegraphics[width=7.6cm,clip]{1964fi17.ps} \end{figure}$	Figure 13: Evolution of r₀ in a VVDS mock survey (filled circles), compared to that of its parent catalog (open circles). Error bars are as explained in the text. The "true'' and "measured'' values of r₀ are very consistent within the error bars, providing an internal proof of the quality of our correction scheme.
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$\begin{figure} \par\includegraphics[width=7.7cm,clip]{1964fi19.ps} \end{figure}$	Figure 15: Measured $w_{\rm p}(r_{\rm p})$ in the case of different number of passes over the same field. When the field is observed only once we are clearly not able to properly recover properly $w_{\rm p}(r_{\rm p})$ on the smallest scale. When we observe the field more times the recovery is much better also on the small scales.
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$\begin{figure} \par\includegraphics[width=7.5cm,clip]{1964fi20.ps} \end{figure}$	Figure 16: Measurements of $\xi (s)$ for a different number of observing "visits'' over the same field.
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