The VIMOS Public Extragalactic Redshift Survey (VIPERS)
The growth of structure at 0.5 < z < 1.2 from redshiftspace distortions in the clustering of the PDR2 final sample ^{⋆}
^{1} INAF–Osservatorio Astronomico di Brera, via Brera 28, 20122 Milano – via E. Bianchi 46, 23807 Merate, Italy
email: andrea.pezzotta@brera.inaf.it
^{2} Dipartimento di Fisica, Università di MilanoBicocca, Piazza della Scienza 3, 20126 Milano, Italy
^{3} Aix Marseille Univ., CNRS, LAM, Laboratoire d’Astrophysique de Marseille, Marseille, France
^{4} Aix Marseille Univ., Univ. Toulon, CNRS, CPT, Marseille, France
^{5} Università degli Studi di Milano, via G. Celoria 16, 20133 Milano, Italy
^{6} Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK
^{7} INAF – Istituto di Astrofisica Spaziale e Fisica Cosmica Milano, via Bassini 15, 20133 Milano, Italy
^{8} INAF–Osservatorio Astronomico di Bologna, via Ranzani 1, 40127, Bologna, Italy
^{9} INAF–Osservatorio Astrofisico di Torino, 10025 Pino Torinese, Italy
^{10} Laboratoire Lagrange, UMR7293, Université de Nice Sophia Antipolis, CNRS, Observatoire de la Côte d’Azur, 06300 Nice, France
^{11} Dipartimento di Fisica e Astronomia – Alma Mater Studiorum Università di Bologna, viale Berti Pichat 6/2, 40127 Bologna, Italy
^{12} Institute of Physics, Jan Kochanowski University, ul. Swietokrzyska 15, 25406 Kielce, Poland
^{13} National Centre for Nuclear Research, ul. Hoza 69, 00681 Warszawa, Poland
^{14} INFN, Sezione di Bologna, viale Berti Pichat 6/2, 40127 Bologna, Italy
^{15} AixMarseille Université, Jardin du Pharo, 58 bd Charles Livon, 13284 Marseille Cedex 7, France
^{16} IRAP, 9 av. du colonel Roche, BP 44346, 31028 Toulouse Cedex 4, France
^{17} Astronomical Observatory of the Jagiellonian University, Orla 171, 30001 Cracow, Poland
^{18} School of Physics and Astronomy, University of St Andrews, St Andrews KY16 9SS, UK
^{19} INAF – Istituto di Astrofisica Spaziale e Fisica Cosmica Bologna, via Gobetti 101, 40129 Bologna, Italy
^{20} INAF – Istituto di Radioastronomia, via Gobetti 101, 40129 Bologna, Italy
^{21} CanadaFranceHawaii Telescope, 65–1238 Mamalahoa Highway, Kamuela, HI 96743, USA
^{22} Dipartimento di Matematica e Fisica, Università degli Studi Roma Tre, via della Vasca Navale 84, 00146 Roma, Italy
^{23} INFN, Sezione diRoma Tre, via della Vasca Navale 84, 00146 Roma, Italy
^{24} INAF–Osservatorio Astronomico di Roma, via Frascati 33, 00040 Monte Porzio Catone (RM), Italy
^{25} Department of Astronomy, University of Geneva, ch. d’Ecogia 16, 1290 Versoix, Switzerland
^{26} INAF–Osservatorio Astronomico di Trieste, via G. B. Tiepolo 11, 34143 Trieste, Italy
^{27} Department of Astronomy & Physics, Saint Mary’s University, 923 Robie Street, Halifax, Nova Scotia, B3H 3C3, Canada
Received: 20 December 2016
Accepted: 19 May 2017
We present measurements of the growth rate of cosmological structure from the modelling of the anisotropic galaxy clustering measured in the final data release of the VIPERS survey. The analysis is carried out in configuration space and based on measurements of the first two even multipole moments of the anisotropic galaxy autocorrelation function, in two redshift bins spanning the range 0.5 <z< 1.2. We provide robust and cosmologyindependent corrections for the VIPERS angular selection function, allowing recovery of the underlying clustering amplitude at the percent level down to the Mpc scale. We discuss several improvements on the nonlinear modelling of redshiftspace distortions (RSD) and perform detailed tests of a variety of approaches against a set of realistic VIPERSlike mock realisations. This includes using novel fitting functions to describe the velocity divergence and density power spectra P_{θθ} and P_{δθ} that appear in RSD models. These tests show that we are able to measure the growth rate with negligible bias down to separations of 5 h^{1} Mpc. Interestingly, the application to real data shows a weaker sensitivity to the details of nonlinear RSD corrections compared to mock results. We obtain consistent values for the growth rate times the matter power spectrum normalisation parameter of fσ_{8} = 0.55 ± 0.12 and 0.40 ± 0.11 at effective redshifts of z = 0.6 and z = 0.86 respectively. These results are in agreement with standard cosmology predictions assuming Einstein gravity in a ΛCDM background.
Key words: cosmology: observations / largescale structure of Universe / galaxies: highredshift / galaxies: statistics
Based on observations collected at the European Southern Observatory, Cerro Paranal, Chile, using the Very Large Telescope under programs 182.A0886 and partly 070.A9007. Also based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA, at the CanadaFranceHawaii Telescope (CFHT), which is operated by the National Research Council (NRC) of Canada, the Institut National des Sciences de l’Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. This work is based in part on data products produced at TERAPIX and the Canadian Astronomy Data Centre as part of the CanadaFranceHawaii Telescope Legacy Survey, a collaborative project of NRC and CNRS. The VIPERS web site is http://www.vipers.inaf.it/
© ESO, 2017
1. Introduction
The discovery of the accelerated expansion of the Universe in the late stages of the twentieth century has given us a selfconsistent standard cosmological model, which is in close agreement with virtually all current cosmological observations. Multiple lines of evidence, such as cosmic microwave background anisotropies (Hinshaw et al. 2013; Planck Collaboration XIII 2016), baryon acoustic oscillations in the galaxy distribution (Beutler et al. 2011; Blake et al. 2011; Anderson et al. 2012), and SNe Ia luminosity distances (Riess et al. 1998; Perlmutter et al. 1999), require most of the energy content of the Universe to be in the form of a repulsive dark energy that is empirically close in behaviour to the classical cosmological constant (see e.g. Weinberg et al.2013, for some history and a review of current constraints). The nature of dark energy is naturally a question of huge interest, with possibilities ranging from a fixed vacuum energy density with equation of state w = P/ρc^{2} = − 1 to dynamical models based on evolving scalar fields varying both in space and time. Such models motivate an effort to measure w and its evolution. But independent of the outcome of this exercise, a puzzle remains that a very large vacuum density seems to be necessary, and hence the much smaller observed value therefore requires a challenging degree of finetuning (Weinberg 1989).
A more radical explanation for the observed acceleration could be that the theory of gravity itself is modified on cosmological scales (Carroll et al. 2004; Jain & Khoury 2010; Clifton 2011). Commonly discussed alternatives include f(R) gravity, where the gravitational Lagrangian is made more complicated than a simple Ricci scalar R; chameleon models that invoke a fifth fundamental force to drive the acceleration; and DGP (DvaliGabadadzePorrati) models, which postulate a higher dimensional Minkowski spacetime, within which the ordinary 3+1 spacetime is embedded. For an appropriate choice of model parameters, dark energy and modified gravity can both reproduce the observed expansion history H(z). In principle this degeneracy can be lifted by measuring the growth rate of cosmic structure. Modifications of gravity involve a variation in the strength of the gravitational force with scale or environment, and thus a key question is whether density fluctuations are growing at the rate predicted by models involving general relativity and a homogeneous dark energy.
Among observational methods to estimate the growth rate of structure, redshiftspace distortions (RSD) in the clustering pattern of galaxies (Kaiser 1987) have assumed a growing importance in the last decade (e.g. Guzzo et al. 2008). These distortions arise when the Doppler effect of galaxy peculiar velocities supplements the isotropic Hubble expansion. Peculiar velocities are inevitably associated with gravitational growth of inhomogeneities, which can be described by the logarithmic growth rate of density perturbations, (1)where δ is the fractional density fluctuation and a is the cosmic scale factor. For many (but not all) theories of gravity, this growth rate can be well approximated by an empirical relation as f(z) = [ Ω(z) ] ^{γ} (Peebles 1980; Lahav et al. 1991), provided the fluctuations are in the linear regime and in the growing mode. For Einstein gravity, γ ≃ 0.55, but this parameter can vary by around 0.1 between different commonly discussed models of latetime dark energy and modified gravity (Dvali et al. 2000; Linder & Cahn 2007). Measurements of linear RSD from galaxy redshift surveys constrain the combination β = f/b, where b is an unknown linear galaxy bias parameter. But the realspace galaxy autocorrelation function, b^{2}ξ_{mass}, is observable, so the combined parameter β can be split to yield an estimate of a quantity that purely concerns dark matter: fσ_{8}, where σ_{8} is the rms linear matter fluctuations within spheres of radius 8 h^{1} Mpc.
Fig. 1 Footprint of the VIPERS observations within the W1 (top) and W4 (bottom) fields, as reconstructed from the final galaxy sample. The VIMOS pointings and quadrants are indicated by black rectangles. Galaxies are colour coded according to their value of the target sampling rate (TSR: see Sect. 4), which can be considered as a proxy for the inverse of the projected galaxy density field. Empty rectangles correspond to failed quadrants, for which the spectroscopic mask insertion failed or was incorrect, leading to no collection of data. 

Open with DEXTER 
Unfortunately, extracting the linear RSD signal from galaxy redshift surveys is nontrivial because much of the RSD signal lies on quasilinear and nonlinear scales. A simple and widely used extension of the linear Kaiser model is the dispersion model (Peacock & Dodds 1994), which accounts for radial convolution by a random velocity dispersion plus nonlinear corrections to the realspace power spectrum. This model was successfully applied to several galaxy surveys in the past (Peacock et al. 2001; Guzzo et al. 2008), but is insufficiently accurate to be trusted when the precision allowed by the data goes below 10% (Okumura & Jing2011; Bianchi et al.2012; see also the companion paper by Wilson et al. 2017). There have been a number of attempts to derive improved RSD models. As shown by Scoccimarro (2004), the dispersion model is a simplification of the original streaming model (Peebles 1980; Fisher 1995), in which the full redshiftspace correlation function is obtained by convolution with a proper scaledependent pairwise velocity distribution. But predicting this distribution function is hard (e.g. Bianchi et al. 2015, 2016; Uhlemann et al. 2015) and typical applications simplify the problem by adopting a scaledependent Gaussian pairwise distribution function (e.g. Reid et al. 2012). Scoccimarro (2004) proposed an influential alternative, in which the linear Kaiser term is generalised by including the velocity and velocitydensity power spectra. This concept was extended by the TNS model (Taruya et al. 2010), which takes better into account the nonlinear coupling between the density and the velocity field. This model is currently considered one of the best descriptions of RSD down to the quasilinear regime.
These theoretical developments have been stimulated by a growing number of new measurements from larger datasets. These included in particular the 6dfGS (Beutler et al. 2012), WiggleZ (e.g. Blake et al. 2011; Contreras et al. 2013), and BOSS (e.g. Reid et al. 2014; Beutler et al. 2017; Satpathy et al. 2017; Sanchez et al. 2017; Grieb et al. 2017). The present paper is one in a series aimed at extending this RSD work to higher redshifts by analysing the final PDR2 release of the VIMOS Public Extragalactic Redshift Survey (VIPERS; Guzzo et al. 2014; Garilli et al. 2014; Scodeggio et al. 2017). This survey has collected redshifts for about 90 000 galaxies in the range 0.4 ≲ z ≲ 1.2 with sampling and volume comparable to those of local surveys, such as the Twodegree Field Galaxy Redshift Survey (2dFGRS) at z ≃ 0.1 (Colless et al. 2001). The prime goal of VIPERS was an accurate measurement of the growth rate of structure at a redshift around unity. An early measurement was performed using the Public Data Release 1 (PDR1; Garilli et al.2014), setting a reference measurement of fσ_{8} at z = 0.8 (de la Torre et al. 2013). Having nearly doubled the sample, this analysis is now revisited and expanded in a number of ways. de la Torre et al. (2017) performs a configuration space joint analysis involving RSD and galaxygalaxy lensing, while Wilson et al. (2017) develops a direct Fourierspace approach coupled with the socalled “clipping” linearisation of the density field. With a similar aim, Mohammad et al. (in prep.) identifies optimal subclasses of RSD tracers, focusing on luminous blue galaxies. The analysis we present here uses the configurationspace information contained in the first two even multipole moments of the anisotropic correlation function, implementing the currently most advanced nonlinear corrections and testing their performances on VIPERSlike mocks.
The paper is organised as follows. In Sect. 2 we give a description of the final VIPERS dataset and of the corresponding mock catalogues used throughout the analysis, while in Sect. 3 we describe the estimation of the twopoint correlation function of galaxies in redshift space. Section 4 describes the target selection biases and how these are mitigated. In Sect. 5 we present the VIPERS measurements. The error estimates are described in Sect. 6 along with the fitting procedure. Section 7 gives a description of the RSD models that are used in Sect. 8 to understand the level of systematics in the recovery of the growth rate of structure. The results are presented in Sect. 9 and discussed in Sect. 10 with our conclusions.
Throughout this analysis, if not specified otherwise, we assume a fiducial flat ΛCDM cosmological model with (Ω_{m},Ω_{b},n_{s}) = (0.30,0.045,0.96) and parametrise the Hubble constant as H_{0} = 100 h km s^{1} Mpc^{1}.
2. The VIPERS survey
2.1. Observations
The VIPERS survey covers an overall area of 23.5 deg^{2} over the W1 and W4 fields of the CanadaFranceHawaii Telescope Legacy Survey Wide (CFHTLSWide). The VIMOS multiobject spectrograph (Le Fèvre et al. 2003) was used to cover these two fields with a mosaic of 288 pointings, 192 in W1 and 96 in W4 (see Fig. 1). Galaxies are selected from the CFHTLS catalogue to a faint limit of i_{AB} = 22.5, applying an additional (r − i) vs. (u − g) colour preselection that efficiently and robustly removes galaxies at z< 0.5. Coupled with a highly optimised observing strategy (Scodeggio et al. 2009), this doubles the mean galaxy sampling efficiency in the redshift range of interest, compared to a purely magnitudelimited sample, bringing it to 47%.
Spectra are collected at moderate resolution (R ≃ 220) using the LR Red grism, providing a wavelength coverage of 5500−9500 Å. The typical redshift error for the sample of reliable redshifts is σ_{z} = 0.00054(1 + z), which corresponds to an error on a galaxy peculiar velocity at any redshift of 163 km s^{1}. These and other details are given in the full PDR2 release accompanying paper (Scodeggio et al. 2017). A discussion of the data reduction and management infrastructure was presented in Garilli et al. (2014), while a complete description of the survey design and target selection was given in the survey description paper (Guzzo et al. 2014). The dataset used in this paper is an early version of the PDR2 data, from which it differs by a few hundred redshifts revised during the very last period before the release. In total it includes 89 022 objects with measured redshifts. As in all statistical analyses of the VIPERS data, only measurements with quality flags 2 to 9 inclusive are used, corresponding to a sample with a redshift confirmation rate of 96.1% (for a description of the quality flag scheme, see Scodeggio et al.2017).
In the analysis presented here we analyse two redshift subsamples of the whole survey (W1 + W4) in the ranges 0.5 <z< 0.7 and 0.7 <z< 1.2, including 30 764 and 35 734 galaxies, respectively, for a total of 66 498 highquality redshifts (out of the total number of 76 552 galaxies of this quality, i.e. with flag ≥2, in the full survey, see Scodeggio et al. 2017).
Fig. 2 Redshift distribution of the final VIPERS galaxy sample. The distributions of redshifts collected separately within the two CFHTLS fields are plotted together with the combined distribution using different colours. The red and purple solid lines show the best fit using the analytic template in Eq. (2) and the predicted V_{max} profile of the combined redshift distribution, respectively. The peculiar distribution of the VIPERS galaxy sample differs from the typical expectation from a magnitudelimited sample. This deviation is the result of the colourcolour preselection adopted to reject most galaxies located at z< 0.5. 

Open with DEXTER 
2.2. Redshift distribution
The redshift distribution of the galaxy sample is shown in Fig. 2. At z> 0.6, it follows the typical decay in the number of objects expected for a magnitudelimited survey, while the rapid fall of the counts at z< 0.5 is the result of the colourcolour preselection. In de la Torre et al. (2013) it was shown that this histogram can be modelled analytically by the functional form (2)where A, z_{0}, α, and β are fitting parameters. The term CSR(z) (colour sampling rate) describes the colour−colour preselection in terms of an error function transitioning between 0 and 1 around redshift z = 0.5, i.e. CSR(z) = (1 − erf[b(z_{t} − z)]) / 2 where the transition redshift z_{t} and the transition width b are free parameters. As shown in Scodeggio et al. (2017), CSR(z) is unity for z ≥ 0.6, corresponding to a purely magnitudelimited selection.
The best fit of Eq. (2) to the final VIPERS data is shown by the red curve in Fig. 2. Such modelling of the redshift distribution is an important and sensitive ingredient when estimating galaxy clustering, as we discuss in Sect. 3 and in de la Torre et al. (2013). We compare it with the V_{max} technique (e.g. Cole 2011; de la Torre et al. 2013) shown in Fig. 2 with the purple curve. Although we find no significant difference in the resulting clustering between the two methods, here we chose to use the V_{max} method, as in the companion paper of de la Torre et al. (2017). A further method often used in the literature is that of smoothing the observed redshift distribution with a Gaussian kernel (as for instance in the parallel papers by Rota et al.2017; and Wilson et al. 2017).
2.3. Mock galaxy samples
In order to test the details of the analysis and the modelling of RSD, we make use of a suite of mock galaxy catalogues designed to be a realistic match to the VIPERS final dataset. These were constructed from the Big MultiDark Nbody simulation (Klypin et al. 2016), which assumes a flat ΛCDM cosmology with (Ω_{m},Ω_{Λ},Ω_{b},h,n_{s},σ_{8}) = (0.307,0.693,0.0482,0.678,0.960,0.823) and covers a volume of 15.625 h^{3} Gpc^{3}. The construction of the mock samples is described in de la Torre et al. (2017) and is based on the method detailed in de la Torre et al. (2013). These papers provide for further detail; we only give a brief overview of the adopted method in the following.
We extracted 153 independent light cones from the simulation volume, which follows the geometry of the VIPERS W1+W4 fields. The dark matter haloes identified in the simulation were populated with galaxies using the halo occupation distribution (HOD) technique. Because of the halo mass resolution of the simulation which is too large to host the faintest galaxies observed in VIPERS, the method of de la Torre & Peacock (2013) was applied to reconstruct haloes below the resolution limit. Each halo was then populated with galaxies according to its mass as described by the HOD. The HOD was calibrated directly on the VIPERS data as presented in de la Torre et al. (2013). To obtain fully realistic VIPERS mocks one needs to reproduce the VIPERS survey selection function. This was carried out following several steps. First, the magnitude cut i_{AB}< 22.5 and the effect of the colour selection on the radial distribution of the mocks were applied. The mock catalogues thus obtained are similar to the parent photometric sample used as target galaxy sample for spectroscopy in VIPERS. The slitpositioning algorithm with the same setting as for the data was further applied to parent mock catalogues. This allows us to reproduce the VIPERS footprint on the sky, the smallscale angular pair incompleteness, and the variation of TSR across the fields. Finally, random redshift errors were added to mock galaxy redshifts, which are similar to those present in the data. This procedure allows us to produce realistic mock galaxy catalogues that contain the detailed survey completeness function and observational biases of VIPERS.
3. Galaxy clustering estimation
We quantify galaxy clustering in redshift space by estimating the anisotropic twopoint correlation function ξ(s,μ), where s is the redshiftspace separation of galaxy pairs and μ is the cosine of the angle between the separation vector and the line of sight. We generate a catalogue of randomly distributed objects subject to the same angular and radial selection as the true data and use the Landy & Szalay (1993) estimator (3)where GG(s,μ), GR(s,μ), and RR(s,μ) are the normalised galaxygalaxy, galaxyrandom, and randomrandom pair counts in bins of s (Δ(log _{10}s) = 0.1) and μ (Δμ = 0.01), respectively. This estimator has been shown to provide a nearly unbiased estimate of the twopoint correlation function, while minimising its variance (Landy & Szalay 1993). We typically use random samples with 30 times more objects than in the true data to reduce their shot noise contribution to a negligible amount.
In this work we estimate the growth rate by fitting RSD models not to the full shape of ξ(s,μ), but rather to its first two even multipole moments, ξ^{(0)}(s) and ξ^{(2)}(s), defined as (4)where ℒ_{ℓ} is the ℓth order Legendre polynomials. Such an approach is normally preferred to prevent the size of data vectors and the resulting covariance matrix from becoming too large for practical computation (but see Mohammad et al.2016, for discussion of some drawbacks of this choice). We do not include in this analysis the extra information potentially provided by the hexadecapole ξ^{(4)}. In addition to being noisier than the lower order moments, we found that our corrections of ξ^{(4)} for observational effects (see next chapter) do not fully recover the correct shape, thus there is a risk of introducing a further systematic bias in the final measurements.
4. Systematic selection effects
The VIPERS angular selection function is the result of combining several different angular completeness functions. Two of these are binary masks, i.e. describing areas that are fully used or fully lost. The first mask is related to defects in the parent photometric sample, which are mostly areas masked by bright stars, and the other mask is associated with the specific footprint of VIMOS and how the different pointings are tailored together to mosaic the VIPERS area. Working in configuration space, these masks are easily accounted for when defining the area and the auxiliary random samples for clustering measurements.
A more complex selection is related to the incomplete target sampling of VIPERS; on average 47% of the targets satisfying the VIPERS selection criteria can be placed behind a slit and observed, defining what we call the average target sampling rate (TSR). In principle, we should also account for the colourcolour preselection of the target sample, which introduces a colour sampling rate (CSR; see Scodeggio et al.2017). In practice, since the CSR can be safely assumed to be constant over the survey area thanks to the particularly careful homogenisation of the parent sample photometry (see Guzzo et al.2014), its effect is absorbed into the fit or model describing the smoothed redshift distribution, as in Eq. (2). In any case, the CSR is consistent with being unity for z ≥ 0.6. Finally, we also have to take into account how the probability of measuring the redshift of a targeted galaxy depends on observational conditions or technical issues, which can be locationdependent, which we call the spectroscopic success rate (SSR). The relative relevance, modelling, and overall impact of all these effects is described in more detail the following sections.
Fig. 3 Diagram of the slit layout of a typical VIMOS pointing of the VIPERS survey (W1P082 in this case) superimposed on the actual DSS finding chart. The open circles with the tiny horizontal slits mark the target objects. The vertical rectangles define the area where the resulting spectrum falls once the dispersing element (grism) is inserted. 

Open with DEXTER 
4.1. Slit collisions
A multiobject spectrograph survey must inevitably face the limitations imposed by the mechanics of how light from the targets is collected on the focal plane. Either fibres or slitlets (as in the case of VIMOS) impose a minimum physical size below which the spectrum of two adjacent galaxies on the sky cannot be collected at the same time. This completely suppresses the smallscale clustering amplitude, unless multiple telescope visits of the same field are performed, which is not the case with VIPERS. Furthermore, the same limit on close pairs causes highdensity regions on the sky to be more poorly sampled with respect to lowdensity regions; this introduces a mismatch that, as we show here, affects the amplitude of clustering on all scales. For VIMOS, this effect is further enhanced by the slitpositioning optimisation software (SPOC; Bottini et al. 2005), which attempts to maximise the number of slits observed in each quadrant and as such tends to homogenise the angular distribution of targets.
Fig. 4 Top: angular correlation function measured from the VIPERS W1 mock samples. In order to enhance the signaltonoise ratio, we show only the mean over 153 realisations. The angular correlation function of the parent and selected sample are shown with a cyan and magenta line, respectively. The two dashed vertical lines indicate the typical angular size of the slits and the raw spectra. Bottom: the completeness function, extracted from the mean of the 153 W1 mock samples (magenta line), is shown. The corresponding quantity measured from the VIPERS dataset is shown with red circles. 

Open with DEXTER 
Furthermore, in a multislit spectrograph such as VIMOS the dispersed spectrum is imaged directly onto the detector. As is evident from Fig. 3, this creates another “forbidden zone” perpendicular to the slit, where no other target can be observed without causing two spectra to overlap; this unlike in fibre spectrographs, where fibres are typically taken away from the telescope to a standing spectrograph and the spectra are conveniently aligned and packed on the CCD. Since the projected length of the spectrum on the detector is much larger than the corresponding size of the slit, this introduces another typical scale below which the number of measured angular pairs is reduced, again limiting the sampling of overdensities on the sky. In VIPERS, the spectral dispersion is always oriented along the northsouth direction, so the depletion of galaxy pairs is anisotropic on the sky and larger along the declination direction.
The impact of these effects on angular clustering is quantified in Fig. 4, where in the top panel we plotted, for both the average of 153 mocks (solid lines) and the VIPERS data (filled points), the angular correlation function of the parent and spectroscopic samples (w_{p}(θ) and w_{s}(θ), respectively). The bottom panel shows instead the ratio of the corresponding numbers of pairs (bottom panel), defined as (5)In this figure we find clear evidence of the two angular scales discussed earlier, which are related to the width and length of the spectra; these are identified in the figure by the vertical dashed lines. The origin of this effect can be better identified if we split the separation angle θ into its components along the right ascension and declination directions, Δ_{RA} and Δ_{DEC}. The angular completeness map C(Δ_{RA},Δ_{DEC}), corresponding to Eq. (5) is shown in Fig. 5. Here the ‘shadow’ of the target spectra is recognisable as the rectangular region with nearly zero counts at small separations. The few residual counts in this area are produced by the small variations in the slit length, together with the effect of the few serendipitous targets observed by chance within the slit of a primary target.
Fig. 5 Twodimensional completeness function deprojected along the orthogonal coordinate axis RADec. The imprint of a single galaxy spectrum is visible in the plot as the almost zero rectangular region at small angular separation. This region corresponds to the typical area occupied by the projected spectrum of an observed source. The enhancement of clustering in the top left region of the plot is produced by the particular displacement along common columns of slits within a quadrant. The scale of the abscissa and the ordinates is very different. 

Open with DEXTER 
Translated to spatial scales, this angular selection function results in a strong suppression of the clustering amplitude below 1 h^{1} Mpc, as shown by the dotted line in Fig. 6. In de la Torre et al. (2013), we corrected for this effect by upweighting each galaxygalaxy pair at a given angular separation θ_{ij} by the inverse of the corresponding value of C(θ_{ij}), i.e. (6)We discuss the effectiveness of this weight together with the correction of the largescale effect of the TSR at the end of the next section.
4.2. Largerscale effects
Along with the drastic suppression at small separations, the physical size of the slits is responsible for the inhomogeneous sampling between high and lowdensity regions across a single VIMOS quadrant. This translates in an almost constant suppression of the clustering amplitude on scales above 1 h^{1} Mpc. The correcting scheme we discuss here builds upon the original approach of de la Torre et al. (2013), in which galaxies are assigned a further weight (7)In that paper, however, the TSR used for each galaxy was simply the average value over the corresponding VIMOS quadrant; in this way, all target galaxies in a quadrant were upweighted by the same factor. As shown by the dotdashed curve in Fig. 6, when considering the realspace correlation function ξ(r) this procedure has limited effect; however when combined with the w^{A}(θ) = 1 /C(θ_{ij}) smallscale boost, the TSR upweighting strategy provides a better correction (see Fig. 8 of de la Torre et al.2013).
The improved correction adopted here uses instead a local estimate of the TSR_{i}, which is defined as the ratio of the local surface densities of target and parent galaxies (i.e. before and after applying the target selection); these are estimated as detailed below and then averaged within an aperture of a given shape and size. If we call these quantities and , the TSR_{i} is defined as (8)The continuous δ fields are obtained, starting from the discrete distributions of parent and target galaxies, using a Delaunay tessellation (Delaunay 1934) to estimate the density at the position of each galaxy, and then linearly interpolating. These two continuous fields are then used to compute the values of and within an aperture of a given shape and size.
Fig. 6 Optimising the correction for the Target Sampling Rate on largescales; the tests are based on the mean of 153 mock samples. Top: systematic error on the realspace twopoint correlation function introduced by the TSR (dotted line), confronted to the results of different strategies to estimate its local value and the corresponding weight (see text for details). Circular apertures with varying radius (r = 90, 70 and 50 arcsec), and a rectangular aperture 60 × 100 arcsec^{2} are compared. The dotdashed line also shows the result of using a weight based only on the quadrantaveraged TSR. Note that here the smallscale further correction based on Eq. (6) has not been applied yet. Bottom: corresponding scatter of the different corrections. To allow comparison with the systematic error, this is also reported, for the rectangular aperture, as the shaded area in the top panel. 

Open with DEXTER 
We identified the bestperforming geometry for this aperture through the tests shown in Fig. 6. The overall correction is remarkable, since we are able to accurately recover the parent ξ(r) at large separations, both with a circular and rectangular aperture. The rectangular aperture provides the best correction to realspace clustering, which can be understood in terms of the anisotropy of the spectral “shadows” discussed earlier. The optimal size of the rectangular aperture is found to be 60 × 100 arcsec^{2}. The resulting distribution of the TSR_{i} values over the survey regions is shown in Fig. 1. One may expect the size of the optimal aperture for estimating the TSR to match to some extent the typical shadow of the VIPERS spectra on the focal plane. By direct test, this is clearly not the case because the typical size of a spectrum defines an aperture that is too small, if one is to make shot noise negligible.
Fig. 7 Impact of the TSR and the SSR on the radial profile of the VIPERS galaxy samples. In the bottom panel we plot the relative difference of the V_{max} fits to the redshift distribution after applying the correction to the same obtained from the observed histogram. Dashed, dotted, and solid lines give the results for W1, W4, and the combined measurement, respectively. The smoothed radial profile is estimated using the V_{max} method. While the TSR does not affect the redshift distribution, the SSR enhances the number counts at z> 0.95. 

Open with DEXTER 
4.3. Redshift dependence of angular corrections
Some of the corrections for angular selection biases also have an effect on the redshift distribution. Figure 7 shows the effect of correcting for the TSR and SSR on the observed redshift distribution of the VIPERS data. While the TSR does not introduce a significant redshift dependence, the application of the SSR boosts the expected number of galaxies in the distant (z> 1) part of the sample. This clearly reflects the increased inefficiency to measure redshifts for more and more distant objects. To be fully consistent with the data, the random samples used for the clustering analyses have to be weighted accordingly.
5. Twopoint correlations from the VIPERS data
We thus proceed to estimate the redshift space correlation function and its moments for the VIPERS survey, adopting the weighting scheme discussed in the previous sections, which we recap for convenience:

Each galaxy is upweighted by the inverse of its TSR defined byEqs. (7) and (8), , as well as by the inverse of its SSR, .

Each galaxygalaxy pair with angular separation θ is upweighted by the angular weight w^{A}(θ) defined in Eqs. (5) and (6).
Pair counts in the twopoint correlation function estimator of Eq. (3) are then expressed as where Θ_{ij}(s,μ) is equal to unity for log (s_{ij}) in [ log (s) − Δlog (s) / 2,log (s) + Δlog (s) / 2 ] and μ_{ij} in [ μ − Δμ/ 2,μ + Δμ/ 2 ], and null otherwise.
Fig. 8 Impact of the target selection effects and their correction on the amplitude of the monopole (left) and quadrupole (right) of the redshiftspace correlation function ξ(s,μ). Considering the mean over 153 mock samples, in the bottom panel we plot the fractional deviation of the multipoles measured with the observed sample from those obtained using the parent catalogue. 

Open with DEXTER 
Fig. 9 Final measurements of the anisotropic redshiftspace correlation function, ξ(r_{p},π) from the final data of the VIPERS survey within the two redshift ranges indicated by the labels. Solid contours correspond to isocorrelation levels of 0.3, 0.5, 1, 2, 5. 

Open with DEXTER 
The final performance of this weighting scheme on the recovered monopole and quadrupole of the redshift space correlation function are shown in Fig. 8 for the two redshift ranges considered in the analysis. The combined correction recovers the amplitude of the monopole at the 2% level, down to the Mpc scale, yielding a quasiunbiased estimate of ξ^{(0)}(s) on all comoving scales that are used for the RSD fitting. As for the quadrupole, we are able to have a reliable measurement of ξ^{(2)}(s) (<5% deviation from the fiducial value) down to a few Mpc. This is an encouraging result; any uncorrected anisotropy from selection effects would be in danger of inducing a spurious contribution to the quadrupole, since this is our main measure of anisotropy.
Figure 9 shows the measurement of the anisotropic correlation function ξ(r_{p},π) obtained from the full VIPERS data at 0.5 <z< 0.7 and 0.7 <z< 1.2. A bin size Δs = 0.5h^{1} Mpc was used in both r_{p} and π directions. We combine the results coming from the two VIPERS fields W1 and W4 simply by summing up the pair counts in each bin of separation and normalising for the total number of objects.
6. Covariance matrix and error estimation
Given the intrinsic correlation among different bins of the twopoint correlation function (and consequently of its multipoles), it is essential to obtain a reliable estimate of the covariance matrix to be used during the fitting procedure. The fit is carried out by performing a maximum likelihood analysis of the data given the RSD model, which can be more easily described as the search throughout the parameter space of the position maximizing the likelihood function ℒ defined as (12)Here the observable y = (ξ^{0},ξ^{2}) is the monopolequadrupole combined vector, Ψ ≡ C^{1} is the precision matrix (the inverse of the covariance matrix), N_{b} is the total number of data points, and indices d and m stand for data and model, respectively.
The covariance matrix C is organised in four blocks corresponding to the monopolemonopole, quadrupolequadrupole and monopolequadrupole cross covariance (two identical blocks in the latter case). The full monopolequadrupole covariance matrix is estimated from the 153 mock realisations as (13)where N_{s} is the number of independent realisations used to estimate the covariance, y is the monopolequadrupole vector, indices i,j run over the data points, and index k runs over different realisations. The mean value is estimated by averaging the measured values from different realisations, namely (14)The corresponding correlation matrices obtained in this way for the two redshift subsamples are shown in Fig. 10.
Fig. 10 Correlation matrices for the combined monopolequadrupole data vector in the low (top) and high (bottom) redshift bin. Correlation matrices are computed as , where C is the covariance matrix estimated from a set of 153 independent mock samples. The bottom left and top right squares correspond to the autocovariance of the monopole s^{2}ξ^{(0)} and the quadrupole s^{2}ξ^{(2)}, respectively, while the remaining squares show the crosscovariance terms. The scales under consideration range from s_{min} = 5 h^{1} Mpc to s_{max} = 50 h^{1} Mpc (from left to right). 

Open with DEXTER 
Given the large number of mock samples, the estimate and the inversion of the covariance matrices can be achieved with good accuracy. However, the use of a finite number of mocks has two implications. Firstly, the estimated precision matrix obtained by taking the inverse of Ĉ is biased with respect to the true one, Ψ, where the difference is wellrepresented by an inverse Wishart distribution. Furthermore, the precision matrix Ψ contains statistical errors that propagate to the parameter space, affecting the derived errors on the cosmological parameters. We follow Percival et al. (2014) and correct for these effects by applying two correction factors. In the first case, we can remove the systematic bias of the precision matrix by rescaling Ĉ^{1} as (15)The latter correction factor involves the total number of data points N_{b} and realisations N_{s}. It takes into account the typical skewness characterising an inverse Wishart distribution and is capable of providing an unbiased estimate of the precision matrix (Hartlap et al. 2007). In the second case, the propagation of errors from the precision matrix to the derived parameters can be corrected by defining (16)and applying the correction factor (17)to the estimated parameter covariance. In the previous equation, N_{p} is the total number of free parameters.
7. Modelling redshiftspace distortions
Redshiftspace distortions arise because the apparent position of galaxies is modified by the Doppler effect of their peculiar velocity v. In this way, the redshiftspace position s of galaxies located at r becomes (18)where a is the scale factor, H(a) is the expansion rate and v_{∥} = v·ê_{∥} is the component of the galaxy peculiar velocity along the line of sight. Invoking mass conservation, the redshiftspace density field δ^{s}(s) can be expressed as a function of its realspace counterpart δ(r) as (19)The targeting of highredshift galaxies in VIPERS means that the largest pair separations are much smaller than the distance from the observer, so we can use the smallangle planeparallel approximation; the Jacobian of the realtoredshift space transformation then reduces to (20)where the normalised velocity field is defined as u(r) = − v(r) / [ faH(a) ]. Substituting this expression inside Eq. (19) it follows that (21)Taking the Fourier transform of this equation and making explicit the dependence on , we obtain (22)The redshiftspace power spectrum can thus be written as (Scoccimarro et al. 1999) (23)with Δu_{∥} = u_{∥}(x) − u_{∥}(x^{′}) and r = x − x^{′}. This last equation completely describes the anisotropies produced by peculiar velocities on the clustering of matter particles at each separation. Here, the only assumption is the planeparallel approximation limit.
It is possible to identify two main regimes within which distortions manifest themselves. At large separations, matter has a coherent flow towards overdense regions. In this regime, the velocity field is mainly irrotational (Bernardeau et al. 2002) and can thus be described by its divergence θ(x) = ∇·u(x). These motions produce a systematic distortion of the largescale distribution along the line of sight. This “Kaiser effect” (Kaiser 1987) is basically produced by the terms inside the square brackets in Eq. (23).
In contrast, within the typical scale of haloes, galaxy orbits cross each other: there is a random dispersion in velocities at a given point, which convolves the redshiftspace structure in the radial direction. The clustering amplitude is thus suppressed on small scales, and structures appear stretched along the line of sight in the socalled “Fingers of God” (Jackson 1972). This effect is mainly generated by the exponential prefactor involving the moment generating function of the velocity field.
Equation (23) is hard to use in its given form, because we lack an analytic formula for the ensemble average term inside the integral, particularly in the strongly nonlinear regime. But a number of simpler approximate forms have been suggested, which aim to provide a satisfactory representation of the redshiftspace power spectrum measured from galaxy surveys:

Kaiser model (Kaiser 1987): within the linear theory approximation, the exponential prefactor can be suppressed since its impact on the largest scales is negligible and θ ∝ δ. If the galaxymatter bias relation is also assumed to be linear (δ_{g} = bδ), it follows that (24)where P_{δδ} = P is the linear realspace matter power spectrum and b is the linear galaxy bias.

Dispersion model (Peacock & Dodds 1994): although the previous model can reproduce the apparent enhancement of clustering at large separations, it fails in the description of the nonlinear regime. The latter can be treated in a phenomenological way by artificially suppressing the linear theory predictions to account for the effect of the Fingers of God. Eq. (24) can thus be written as (25)where D(kμσ_{12}) is an analytical damping factor. This term depends on a nuisance parameter σ_{12}, which plays the role of a pairwise velocity dispersion. The basic assumption of the dispersion model is that σ_{12} is not scaledependent, but rather can be fitted as a free parameter. An useful extension of this model is to replace the linear P_{δδ} by a nonlinear version (using an analytic approximation such as HALOFIT). This then allows the dispersion model to give the correct prediction for μ = 0: such modes run transverse to the line of sight and undergo no RSD effect. Some of the alternatives discussed here fail to match the realspace power exactly at μ = 0; this is because they are attempting the harder task of predicting the nonlinearities, rather than taking them from a fit to Nbody simulation data.
Scoccimarro model (Scoccimarro 2004): as soon as the mildly nonlinear regime is entered, the density and velocity divergence fields must be treated separately to account for the nonlinear mode coupling between them. The ansatz proposed by Scoccimarro is that the exponential prefactor inside Eq. (23) can be decoupled from the Kaiser term, so that its impact on the clustering is limited only to the smallest scales. In this case, it can be replaced with a damping factor similar to that already used in the dispersion model, leading to (26)where P_{δθ} and P_{θθ} are the densityvelocity divergence crossspectrum and the velocity divergence autospectrum, respectively. When applying this (and the following) model to real data, these quantities cannot be obtained from the data under analysis. As such, applications of this (and the following) model have used empirical fitting functions calibrated using numerical simulations (Jennings et al. 2011). In a parallel paper (Bel et al., in prep.), we used a large set of Nbody simulations in different cosmologies (the DEMNUni simulations; Carbone et al. 2016), to derive the following, more general set of fitting formulae: where P^{lin}(k) is the linear matter power spectrum and k^{∗} is a parameter representing the typical damping scale of the velocity power spectra. The latter is well described as (29)where p_{1},p_{2} are the only free parameters of the fit. These forms for P_{δθ} and P_{θθ} have valuable, physically motivated properties: they naturally converge to P_{δδ}(k) in the linear regime, including a dependence on redshift through σ_{8}(z). Full details on the derivation and performances of these fitting formulas are presented in Bel et al. (in prep.). Their use in the analysis presented in the following sections is a significant improvement over previous applications of the Scoccimarro and TNS (Taruya et al. 2010) models, as it allows us to extend our tests to smaller scales and apply the models to a higher redshift, as sampled by VIPERS.

Taruya (or TNS) model (Taruya et al. 2010): the nonlinear mode coupling between the density and velocity divergence fields is responsible for a systematic bias between measurements of the power spectrum and its prediction using the previous RSD model. The origin of this deviation is the additional terms inside Eq. (26), which are not accounted for within the previous ansatz. The corrected model can be written as (30)where C_{A} and C_{B} are terms derived using perturbation theory, which aim to account for the density and velocity divergence couplings with the exponential prefactor in Eq. (23). This model bears strong advantages, as it potentially can break the f − σ_{8} degeneracy at the expense that it is intrinsically more difficult to implement. See de la Torre & Guzzo (2012) for the details of its application to biased tracers.
Models in Eqs. (25), (26), and (30) are all tested in the next sections to understand their impact on the recovery of the growth rate. In all cases, at each step of our Monte Carlo Markov chains we generate the full anisotropic redshiftspace power spectrum. For this we make use of CAMB with the latest HALOFIT prescription for the nonlinear P_{δδ} (Takahashi et al. 2012), and Eqs. (27) and (28) to generate the P_{δθ} and P_{θθ} power spectra. The normalisation of the latter realspace power spectra, which can be set by σ_{8}, is degenerate with f and b. This is why one generally parametrises RSD models in terms fσ_{8} and bσ_{8} parameters. In the case of the TNS model, however, this is not possible directly since the C_{A} term involves subterms that are not multiples of the fσ_{8} or bσ_{8} parameters (e.g. Taruya et al. 2010; de la Torre & Guzzo 2012). Therefore for the TNS model, and for the others for consistency, we decide to treat f, b, σ_{8}, σ_{12} as independent parameters in the fit, and provide derived constraints on fσ_{8} a posteriori from the MCMC chains.
In contrast to the implementation discussed in de la Torre & Guzzo (2012), a linear bias is assumed here in the modelling. Given the galaxy population sampled by VIPERS, the redshift range and the scales that are included in the final fits (s> 5 h^{1} Mpc), this is a reasonable assumption (see Fig. 8 in de la Torre & Guzzo 2012). This assumption is relaxed in the parallel complementary RSD analysis of the same VIPERS data by de la Torre et al. (2017), where a full nonlinear bias model is adopted to describe simultaneously clustering and galaxygalaxy lensing down to small separations.
It is important to emphasise that σ_{8}(z) not only plays a role in shaping the C_{A} term, it also controls the level of nonlinearity in P_{δδ}, P_{δθ}, and P_{θθ}. In particular for P_{δδ}, the HALOFIT nonlinear correction to the linear matter power spectrum is computed at each step of the MCMC according to the tested value of σ_{8}(z). This represents a significant improvement over what is usually done in RSD analyses, where σ_{8}(z) is fixed to its fiducial value for the description of P_{δδ}.
In the end, we measure the Fourierspace multipole moments as(31)and convert them to their configuration space counterparts as (32)where j_{ℓ} denotes the spherical Bessel functions.
Adopted priors on the sampling parameters.
Values of the growth rate and related parameters in the two redshift subsamples obtained by fitting the monopole and quadrupole correlation functions over the range 5 h^{1} Mpc <s< 50 h^{1} Mpc, using the TNS model. Central values and 68% marginalised errors on σ_{12}, fσ_{8}, and bσ_{8} are reported.
Finally, we do not attempt to account in our modelling for the AlcockPaczynski (AP) effect (Alcock & Paczynski 1979), i.e. the geometrical distortion introduced on the measured statistical quantities by the choice of a wrong expansion history when transforming angles and redshifts into comoving coordinates. This would require the inclusion of two extra parameters, allowing us in principle to constrain the expansion rate H(z) and the angular diameter distance D_{A}(z). This is explored in two other parallel RSD analyses using the VIPERS data. In Wilson et al. (2017) joint constraints on fσ_{8} and the AP parameter F_{AP} ≡ (1 + z)D_{A}H(z) /c are presented, clearly at the expense of a larger uncertainty on the recovered value of fσ_{8}. In de la Torre et al. (2017) we also explore the impact of the APRSD degeneracy, but adding the extra constraint of galaxygalaxy lensing. Here we instead work with observed quantities computed in the fiducial cosmology, if not for the single parameter σ_{8}, which determines the shape of the nonlinear density and velocity spectra to some extent. In this respect, changing the fiducial cosmological model has a very small impact on the recovered growth rate, for example less than 1% when moving from a WMAP9like to a Plancklike cosmology (de la Torre et al. 2013). The bottom line is that dynamical distortions dominate over the AP effect, as directly shown in earlier works (Marulli et al. 2012; Guzzo et al. 2008).
8. Tests of RSD models
We test in this section the RSD models introduced previously on our set of N_{s} = 153 mocks. In practice, analysing each mock and averaging the measurements would be computationally infeasible, considering the large number of configurations to be tested. We thus chose to average the monopole and quadrupole measurements over the mocks, scale the covariance matrix properly, and fit the models to these average measurements. The aim is to reach a statistical uncertainty that is a factor smaller than a single VIPERS survey to be able to detect potential systematics as small as 1%. This process is more revealing and can show how well a given model performs in recovering the detailed shapes of the quadrupole and monopole correlation function.
We perform likelihood analyses of the mock mean measurements in different configurations, starting with the ideal case and moving on to the case in fully realistic conditions. All likelihood analyses are carried out using an MCMC code, whose output has been crosschecked with the independent MCMC code used in de la Torre et al. (2017). We select flat priors for the full set of free parameters, using boundaries that allow a large set of latetime evolution cosmological models to be considered as possible alternatives to standard ΛCDM. The full list of priors is shown in Table 1, while the bestfit values for the parameters are listed in Table 2. We vary the minimum scale s_{min} of the fit to understand how to select the bestfitting range for the VIPERS data – we expect all RSD models to fail at sufficiently small and nonlinear scales. The maximum scale of the fit is fixed at s_{max} = 50 h^{1} Mpc, above which errors on the VIPERS measured monopole and quadrupole become too large.
8.1. Ideal case
We first study the ideal case that neglects the complex VIPERS angular selection function by using the parent mocks. Redshift errors are also not considered here, to understand how different RSD models behave in the absence of any observational bias. Furthermore, here we consider mock measurements from the full redshift range probed by VIPERS, i.e. 0.5 <z< 1.2, to maximise the available volume for the test; however, we include, in this way, a range of redshift over which we know that galaxies and largescale structures do evolve. The relative difference of the recovered fσ_{8} with respect to the fiducial one is shown in the top panel of Fig. 11. Two types of smallscale damping factor D(kμσ_{12}) are tested: the Lorentzian (filled symbols) and Gaussian (dashed lines) forms.
The overall trend of models using Lorentzian damping favours the TNS model, which yields almost unbiased measurements of the growth rate down to s_{min} = 5 h^{1} Mpc. Some overestimation is however seen for minimum scales close to s_{min} = 8 h^{1} Mpc, which in fact corresponds to the zerocrossing scale of the quadrupole ξ^{(2)}(s). In contrast, both dispersion and Scoccimarro models consistently underestimate the growth rate with an error that fluctuates between 5–10%.
We also check (bottom panel) how the models perform when we fix the shape of the nonlinear density and velocity divergence power spectra to the fiducial cosmology of the mocks through a fixed value for σ_{8}. Reducing the degrees of freedom does reduce the statistical error bars, but has a negative impact on the performance of the TNS model; indeed this model shows a stable positive systematic error of ~5% also when fitting large scales (s_{min}> 25 h^{1} Mpc) only.
Fig. 11 Relative systematic error on the measurement of the linear growth rate from the mean of 153 mock samples, as a function of the minimum fitting scale s_{min}, using the three models discussed in the text. The maximum scales fitted s_{max} is always fixed at 50 h^{1} Mpc. Here the parent mock samples are used to focus on the intrinsic performances of the models. The filled symbols correspond to the use of a Lorentzian form for the nonlinear damping factor in the models, whereas dashed lines correspond to a Gaussian form. In the top panel the full model as described in the text has been used. In the bottom panel, symbols are as above, but σ_{8} (and thus the shape of the nonlinear density and velocity divergence power spectra) has been fixed to the cosmology of the mock samples to test the effectiveness of the RSD model under ideal conditions. In this case, reducing the degrees of freeedom does reduce the statistical error bars, but for the TNS model seems to introduce a positive systematic error of ~5%, this is also the case for s_{min} as large as 25 h^{1} Mpc. 

Open with DEXTER 
Fig. 12 Comparison between the bestfit models for the monopole and quadrupole on the averaged parent mocks using different combinations of RSD models and damping factors. The fit uses all separations down to s_{min} = 5 h^{1} Mpc. The use of a Gaussian damping in the models clearly dramatically worsens the accuracy of the fit, in particular for the largescale quadrupole signal. 

Open with DEXTER 
Returning to the top panel, we also see that in all the cases the choice of a Lorentzian damping yields smaller systematic deviations than with a Gaussian damping. This is reflected by the trend of the dashed lines, which are close to the corresponding markers only when the minimum fitting separation s_{min} is larger than ~15 h^{1} Mpc, while rapidly deteriorating when smaller separations are included in the fit. This is highlighted in Fig. 12, where the different bestfitting models for the monopole and quadrupole using s_{min} = 5 h^{1} Mpc are directly compared to the mock data. Using a Gaussian damping, the model is no longer able to provide a good description of ξ^{(0)} and ξ^{(2)}. Actually, the fit is mostly dominated by the small scales, whose data points have the smallest errors, and this explains why separations above 7 h^{1} Mpc are apparently those giving the strongest deviation between model and data. This result is in close agreement with previous work on the subject (e.g. Bianchi et al. 2012; de la Torre & Guzzo 2012).
8.2. Case with redshift errors
So far no redshift error was assumed in the mock samples. However, real VIPERS redshifts have significant uncertainties, which clearly impact observed redshiftspace distortions. We know from the multiple redshift measurements (Scodeggio et al. 2017) that the redshift error probability distribution for the VIPERS sample of reliable redshifts used here is well described by a Gaussian with standard deviation σ_{z} = 0.00054(1 + z). This corresponds to a dispersion in galaxy peculiar velocity of 163 km s^{1}.
By applying random errors to mock galaxy redshifts following the VIPERS observed distribution, we can effectively see additional distortions. These are shown in Fig. 8.2, where one can see how the shapes of the monopole ξ^{(0)}(s) and the quadrupole ξ^{(2)}(s) are affected. The imprint of redshift errors is similar to that of a smallscale damping of the power spectrum. While the monopole is damped below 4 h^{1} Mpc, the quadrupole is corrupted over a range extending out to ~20 h^{1} Mpc. Clearly, this effect needs to be carefully handled or modelled, if one hopes to recover an unbiased value for the growth rate. The consequences of not correcting for this effect are shown by the dashed lines in Fig. 14, where we repeat the tests of Fig. 11, but now include redshift errors. As feared, there is a significant deviation from the values of fσ_{8} previously measured with the models in the best configuration, i.e. with the Lorentzian damping.
Rather than correcting for redshift errors in the measurements, as performed for the angular selection selection, it is better to include it in the modelling. It is intuitive to supplement the models with a convolution by an extra Gaussian distribution with standard deviation fixed to the VIPERS rms value of σ_{z} = 163 km s^{1}, which corresponds to (33)in terms of lineofsight comoving separation. The filled symbols in Fig. 14 show how with this extra damping term we recover a performance similar to the more idealised case of Fig. 11.
We therefore adopt the TNS model with Lorentzian damping and Gaussian error damping, as our reference model for the final RSD analysis of the VIPERS data. However, we also test, for consistency, the behaviour of the other two models on the actual data to verify whether the trends seen in the mocks are confirmed.
Fig. 13 Effect of redshift errors on the recovered monopole and quadrupole from the galaxy mocks, obtained by adding to the mock redshifts a random Gaussian deviate with dispersion equal to the rms redshift error of the VIPERS. 

Open with DEXTER 
Fig. 14 Same as Fig. 11, but now including Gaussian redshift errors with dispersion equal to the rms value measured for the VIPERS data, added to the mock galaxy redshifts. Here the dashed lines correspond to the use of a Lorentzian damping only, which in Fig. 11 was found to perform at best. With redshift errors, this needs to be supplemented by a further Gaussian damping factor, with dispersion fixed to the above rms error value, to yield the values described by the filled symbols. 

Open with DEXTER 
Fig. 15 Same as Fig. 14, but now using the fully realistic observed mocks, on which all observational effects (masks, SPOC selection, and redshift errors) were included. As before, error bars correspond to the error on the average of the 153 samples. 

Open with DEXTER 
Fig. 16 Monopole and quadrupole of ξ(r_{p},π) for the two redshift subsamples of the final VIPERS dataset (solid points), together with the final bestfitting curves obtained using the TNS model, corresponding to the values reported in Table 2. The likelihood computation has used data down to s_{min} = 5 h^{1} Mpc, as indicated by the tests. Error bars are 1σ deviations and correspond to the dispersion of the mock measurements. Each of these is also shown as a faint background line. 

Open with DEXTER 
8.3. Fully realistic case
We now turn to the case including fully realistic observing conditions. This means considering the target selection (masks, TSR, SSR, etc.) and limiting the samples to the same redshift ranges covered by the data and including redshift errors. The results that we obtain are shown in Fig. 15. The trends of the systematic error as a function of s_{min} are less stable than in the previous case, although the general behaviour and relative performances of the models are the same. The variations give us an idea of the impact of the selection function on samples this size. Again, we see some instability in the TNS model (at least in the bin 0.7 <z< 1.2) when the minimum scale of the fit is chosen around s_{min} ≃ 8 h^{1} Mpc. When we look into the MCMC results in more detail, we see that in this case the Markov chain tends to drift towards unrealistic values of σ_{8}, which are outside of the prior range defined in Table 2. This seems to be related to the difficulty of the TNS model to reach a stable fit in the region where the quadrupole crosses zero. As soon as we include smaller scales (or we move to larger scales), the regular trend is recovered. Nevertheless, we confirm the TNS model as the bestperforming model with systematics ≲5% down to the smallest probed minimum scales.
Overall, the different tests performed on the mock catalogues indicate that we can safely use the TNS model with the appropriate damping functions as well as with a minimum fitting scale of s_{min} = 5 h^{1} Mpc. This is the configuration that we adopt for the analysis of the data.
9. VIPERS RSD results
We present in this section the results of the RSD analysis of the VIPERS final dataset. We apply the methodology described in the previous sections to the VIPERS galaxy sample, including the priors on the sampling parameters reported in Table 1. Since f and σ_{8} are treated as separate parameters in the modelling, despite their intrinsic degeneracy the choice of priors on these two parameters is particularly important. Specifically, the most sensitive prior is that on σ_{8}, as this is the main parameter entering the nonlinear modelling of RSD.
To define a sensible and realistic prior, while allowing room for deviations from GR, we based our choice on the effective field theory of dark energy formalism (Gubitosi et al. 2013; Bloomfield et al. 2013; Gleyzes et al. 2013), which allows a description of various kinds of dark energy models and modifications of gravity to be expressed in a selfconsistent framework that includes the growth rate of structure (Piazza et al. 2014; Perenon et al. 2015). The latter works show that the range spanned by σ_{8}(z) for stable theories can vary significantly, suggesting a range [0.2, 0.65] as appropriate to account for early and latetime dark energy models at the redshifts covered by VIPERS (for definitions, see Perenon et al.2017). Although this choice might exclude some more extreme modified gravity models from being selected by the fit, at the same time it avoids nonphysical degeneracies that arise in the likelihood for some particular values of σ_{8} outside of this range. This choice is corroborated by our parallel complementary analysis using the same data by de la Torre et al. (2017), in which the combination of RSD with galaxygalaxy lensing constrains directly σ_{8}(z), allowing a broader prior at the outset. The bestfit values of σ_{8} that we obtain for the different values of s_{min} are all well inside the adopted prior range with the wings of the posterior distribution pushed against the boundaries only for the largest values of s_{min}, where the signal from the data is the weakest.
Fig. 17 Left panels: measured values of fσ_{8} from the VIPERS survey in the two redshift bins, using the TNS model in its optimal setup that we derived in section 8 (double damping factor: free Lorentzian + fixed Gaussian), as a function of the minimum fitting scale s_{min}. The maximum fitting scale s_{max} is always fixed at 50 h^{1} Mpc. The shaded area gives the statistical error at each s_{min} for the TNS model, as derived from the mocks. Right panels: the same measurements performed on the average of the mocks, i.e. plotting the results of Fig. 15 but showing explicitly the recovered values of fσ_{8}, to ease comparison with the data results on the left. 

Open with DEXTER 
Thus, finally combining the f and σ_{8} measurements obtained using our standard configuration (s_{min} = 5 h^{1} Mpc), we obtain fσ_{8}(z = 0.6) = 0.55 ± 0.12 and fσ_{8}(z = 0.86) = 0.40 ± 0.11. We consider these as our reference measurements in this work and discuss their cosmological implications in the next section. The measurements and bestfitting model monopole and quadrupole correlation functions obtained in the two considered redshift bins are shown in Fig. 16. The corresponding bestfit values for the derived parameters are reported in Table 2.
It is interesting to verify a posteriori whether the trends and relative RSD model performances as a function of s_{min} established from the mock catalogues are similar to those seen in the real data. It is of course clear that any trend is less significant, as the data are statistically equivalent to considering just one of the 153 mock catalogues. In the left panel of Fig. 17, we show the result of this exercise, where the measured values of fσ_{8} as a function of s_{min} are shown for the different tested models. To ease comparison, we reported in the right panel using the same scale, the corresponding results from the mock test for the realistic case (i.e. those of Fig. 15). Apart from the different statistical errors, the three tested RSD models provide virtually identical results in the real data, as opposed to the behaviour seen in the mock catalogues.
Fig. 18 One and twodimensional posterior likelihood distribution of the derived parameters fσ_{8}, bσ_{8} and σ_{12} for the 0.5 <z< 0.7 redshift bin. This corresponds to the result of the analysis of VIPERS data using the dispersion model, the Scoccimarro model, the Taruya model, and s_{min} = 5h^{1} Mpc. The dark and lightshaded areas correspond to the 68% and 95% joint twoparameter confidence levels, respectively. The lower redshift sample shows comparable contours and shapes. 

Open with DEXTER 
Moreover, it seems that in the data the variation of the fσ_{8} measurements with minimum scale are not driven by the adequacy of the model down to those scales, but rather by statistical uncertainties in the measured galaxy correlation functions. The similarity in the results obtained from the different models is confirmed directly by the values of the reduced χ^{2}, which turn out to be very similar.
Fig. 19 Plot of fσ_{8} vs. redshift, showing the VIPERS results together with a compilation of recent measurements. The previous results from the VVDS (Guzzo et al. 2008), SDSSMGS (Howlett et al. 2015), SDSSLRG (Samushia et al. 2012), WiggleZ (Blake et al. 2012), BOSS (Reid et al. 2012), 6dFGS (Beutler et al. 2012), and FastSound (Okumura et al. 2016) surveys are shown with the different symbols (see inset). The solid curve and associated error correspond to the prediction for general relativity in a ΛCDM model set to Planck 2015 cosmological parameters (Planck Collaboration XIII 2016). 

Open with DEXTER 
By directly looking at the posterior likelihood distributions of the parameters obtained with the three models in Fig. 18 (for the highredshift bin), we can see that each model provides slightly different parameter degeneracies, although after marginalisation, fσ_{8} posterior likelihood distributions are almost identical for the three RSD models, with only a slightly larger statistical uncertainty with the TNS model. However, some trends seen in the mock results are recognised in the data, as for example the preference of the TNS model in the highredshift sample to deliver higher values of fσ_{8} when s_{min} is close to the zerocrossing scale of the quadrupole.
Finally, it is important to emphasise the global nonlinear approach to RSD that has been used in this analysis. We used rather small nonlinear scales in the fit and by adopting consistent modelling for the nonlinearities in the realspace density and velocity divergence power spectra, we can obtain further cosmological insight. The level of nonlinearity in our analysis is controlled by one single parameter, σ_{8}(z), and we find that by letting this parameter vary, we can partly break the standard degeneracy that dominates on linear scales between f, σ_{8}, and b parameters. If we marginalise the posterior likelihood function over the σ_{12}, σ_{8}, b parameters, we obtain the following direct growth rate and σ_{8} constraints: [ f(z = 0.6),σ_{8}(z = 0.6) ] = [ 1.048 ± 0.199,0.528 ± 0.076 ] and [ f(z = 0.86),σ_{8}(z = 0.86) ] = [ 0.742 ± 0.179,0.539 ± 0.068 ]. A similar approach has been adopted in de la Torre et al. (2017), where this is strengthened by additional constraints from galaxygalaxy lensing. In particular, the latter allows the improvement of σ_{8} constraints while keeping similar uncertainties on f. A detailed discussion of these results is given in de la Torre et al. (2017). Overall, these findings demonstrate the additional constraining power encapsulated in quasilinear scales, which can be used to break degeneracies and further improve the precision of measurement of the growth rate of structure.
10. Discussion and conclusions
The measurements of the growth rate of structure times σ_{8} that we obtained are
These values are confronted in Fig. 19 with different measurements, including results from other surveys, the VIPERS earlier PDR1 dataset, and parallel works analysing analogous subsets of the VIPERS PDR2 dataset with complementary techniques. It may look surprising that there is no appreciable improvement in the error bars between the former measurement from the PDR1 (de la Torre et al. 2013, red circle) and the new PDR2 estimate in a comparable redshift bin despite a ~30% increase in the sample size. As discussed in de la Torre & Guzzo (2012), this is essentially a price to pay for the more sophisticated treatment of nonlinear effects through the TNS model, which increases the degrees of freedom.
The parallel PDR2 results include measurements obtained from the combination of RSD with galaxygalaxy lensing (de la Torre et al. 2017) or using the voidgalaxy crosscorrelation (Hawken et al. 2017). In forthcoming papers, we shall additionally present further pieces of this combined approach, using specific colourselected subsamples (Mohammad et al., in prep.) or the linearised density field in Fourier space (Wilson et al. 2017), to minimise the need for nonlinear corrections. All these papers represent complementary approaches towards understanding the current limitations we face in our ability to extract in practice the value of these parameters from the modelling of RSD.
The values measured by these different techniques on the same VIPERS data as well as from other surveys at similar redshifts are virtually all compatible within 1σ and agree with the predictions of a ΛCDM model governed by Einstein gravity. But on a larger sample, with much smaller statistical errors, greater care would be needed to test for possible systematic biases that might still be hidden in one or more of the analyses. The application of such a variety of approaches to VIPERS has been made possible by the specific properties of the survey, in particular its dense sampling and rich content of information. With a sparse sampled survey, which is the approach of most of the cosmologically oriented surveys, it would have been impossible to characterise accurately the density field and apply the clipping linearisation technique of Wilson et al. (2017) or to detect reliably cosmic voids such as those used in Hawken et al. (2017). At the same time, a survey with limited imaging information would not permit the investigation of the selection of optimal subpopulations (or the combination of different subpopulations), as we are pursuing in Mohammad et al. (in prep.) or to exploit the combination of RSD with lensing, as we have carried out in de la Torre et al. (2017) and which should be exploited to the fullest by Euclid mission (Laureijs et al. 2011) in the next decade. We therefore believe that the detailed investigation of the properties of RSD within VIPERS should serve as a valuable foundation for nextgeneration studies of greater statistical power.
Acknowledgments
We thank M. Wilson for useful comments. We acknowledge the crucial contribution of the ESO staff for the management of service observations. In particular, we are deeply grateful to M. Hilker for his constant help and support of this programme. Italian participation in VIPERS has been funded by INAF through PRIN 2008, 2010, 2014 and 2015 programmes. L.G. and B.R.G. acknowledge support from the European Research Council through grant No. 291521. O.L.F. acknowledges support from the European Research Council through grant No. 268107. J.A.P. acknowledges support of the European Research Council through the COSFORM ERC Advanced Research Grant (# 670193). G.D.L. acknowledges financial support from the European Research Council through grant No. 202781. R.T. acknowledges financial support from the European Research Council through grant No. 202686. A.P., K.M., and J.K. have been supported by the National Science Centre (grants UMO2012/07/B/ST9/04425 and UMO2013/09/D/ST9/04030). E.B., F.M. and L.M. acknowledge the support from grants ASIINAF I/023/12/0 and PRIN MIUR 20102011. L.M. also acknowledges financial support from PRIN INAF 2012. S.D.L.T. and M.P. acknowledge the support of the OCEVU Labex (ANR11LABX0060) and the A*MIDEX project (ANR11IDEX000102) funded by the “Investissements d’Avenir” French government program managed by the ANR. Research conducted within the scope of the HECOLS International Associated Laboratory, supported in part by the Polish NCN grant DEC2013/08/M/ST9/00664. T.M. and S.A. acknowledge financial support from the ANR Spin(e) through the French grant ANR13BS050005.
References
 Alcock, C., & Paczynski, B. 1979, Nature, 281, 358 [NASA ADS] [CrossRef] [Google Scholar]
 Anderson, L., Aubourg, E., Bailey, S., et al. 2012, MNRAS, 427, 3435 [NASA ADS] [CrossRef] [Google Scholar]
 Bernardeau, F., Colombi, S., Gaztañaga, E., & Scoccimarro, R. 2002, Phys. Rep., 367, 1 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
 Beutler, F., Blake, C., Colless, M., et al. 2011, MNRAS, 416, 3017 [NASA ADS] [CrossRef] [Google Scholar]
 Beutler, F., Blake, C., Colless, M., et al. 2012, MNRAS, 423, 3430 [NASA ADS] [CrossRef] [Google Scholar]
 Beutler, F., Seo, H.J., Saito, S., et al. 2017, MNRAS, 466, 2242 [NASA ADS] [CrossRef] [Google Scholar]
 Bianchi, D., Guzzo, L., Branchini, E., et al. 2012, MNRAS, 427, 2420 [NASA ADS] [CrossRef] [Google Scholar]
 Bianchi, D., Chiesa, M., & Guzzo, L. 2015, MNRAS, 446, 75 [NASA ADS] [CrossRef] [Google Scholar]
 Bianchi, D., Percival, W. J., & Bel, J. 2016, MNRAS, 463, 3783 [NASA ADS] [CrossRef] [Google Scholar]
 Blake, C., Brough, S., Colless, M., et al. 2011, MNRAS, 415, 2876 [NASA ADS] [CrossRef] [Google Scholar]
 Blake, C., Brough, S., Colless, M., et al. 2012, MNRAS, 425, 405 [NASA ADS] [CrossRef] [Google Scholar]
 Bloomfield, J. K., Flanagan, É. É., Park, M., & Watson, S. 2013, JCAP, 1308, 010 [NASA ADS] [CrossRef] [Google Scholar]
 Bottini, D., Garilli, B., Maccagni, D., et al. 2005, PASP, 117, 996 [NASA ADS] [CrossRef] [Google Scholar]
 Carbone, C., Petkova, M., & Dolag, K. 2016, J. Cosmol. Astropart. Phys. Rev., 7, 034 [NASA ADS] [CrossRef] [Google Scholar]
 Carroll, S. M., Duvvuri, V., Trodden, M., & Turner, M. S. 2004, Phys. Rev. D, 70, 043528 [NASA ADS] [CrossRef] [Google Scholar]
 Clifton, T. 2011, Prog. Part. Nucl. Phys., 66, 181 [NASA ADS] [CrossRef] [Google Scholar]
 Cole, S. 2011, MNRAS, 416, 739 [NASA ADS] [CrossRef] [Google Scholar]
 Colless, M., Dalton, G., Maddox, S., et al. 2001, MNRAS, 328, 1039 [NASA ADS] [CrossRef] [Google Scholar]
 Contreras, C., Blake, C., Poole, G. B., et al. 2013, MNRAS, 430, 924 [NASA ADS] [CrossRef] [Google Scholar]
 de la Torre, S., & Guzzo, L. 2012, MNRAS, 427, 327 [NASA ADS] [CrossRef] [Google Scholar]
 de la Torre, S., & Peacock, J. A. 2013, MNRAS, 435, 743 [NASA ADS] [CrossRef] [Google Scholar]
 de la Torre, S., Guzzo, L., Peacock, J. A., et al. 2013, A&A, 557, A54 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 de la Torre, S., Jullo, E., Giocoli, C., et al. 2017, A&A, submitted [arXiv:1612.05647] [Google Scholar]
 Delaunay, B. 1934, Bulletin de l’Academie des Sciences de l’URSS, Classe des sciences mathematiques et naturelles, 6, 793 [Google Scholar]
 Dvali, G., Gabadadze, G., & Porrati, M. 2000, Phys. Lett. B, 485, 208 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
 Fisher, K. B. 1995, ApJ, 448, 494 [NASA ADS] [CrossRef] [Google Scholar]
 Garilli, B., Guzzo, L., Scodeggio, M., et al. 2014, A&A, 562, A23 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Gleyzes, J., Langlois, D., Piazza, F., & Vernizzi, F. 2013, JCAP, 1308, 025 [NASA ADS] [CrossRef] [Google Scholar]
 Grieb, J. N., Sánchez, A. G., SalazarAlbornoz, S., et al. 2017, MNRAS, 467, 2085 [NASA ADS] [Google Scholar]
 Gubitosi, G., Piazza, F., & Vernizzi, F. 2013, JCAP, 1302, 032 [NASA ADS] [CrossRef] [Google Scholar]
 Guzzo, L., Pierleoni, M., Meneux, B., et al. 2008, Nature, 451, 541 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 Guzzo, L., Scodeggio, M., Garilli, B., et al. 2014, A&A, 566, A108 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Hartlap, J., Simon, P., & Schneider, P. 2007, A&A, 464, 399 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Hawken, A. J., Granett, B. R., Iovino, A., et al. 2017, A&A, in press, DOI: 10.1051/00046361/201629678 [Google Scholar]
 Hinshaw, G., Larson, D., Komatsu, E., et al. 2013, ApJS, 208, 19 [NASA ADS] [CrossRef] [Google Scholar]
 Howlett, C., Ross, A. J., Samushia, L., Percival, W. J., & Manera, M. 2015, MNRAS, 449, 848 [NASA ADS] [CrossRef] [Google Scholar]
 Jackson, J. C. 1972, MNRAS, 156, 1P [NASA ADS] [CrossRef] [Google Scholar]
 Jain, B., & Khoury, J. 2010, Annals of Physics, 325, 1479 [NASA ADS] [CrossRef] [Google Scholar]
 Jennings, E., Baugh, C. M., & Pascoli, S. 2011, MNRAS, 410, 2081 [NASA ADS] [Google Scholar]
 Kaiser, N. 1987, MNRAS, 227, 1 [NASA ADS] [CrossRef] [Google Scholar]
 Klypin, A., Yepes, G., Gottlöber, S., Prada, F., & Heß, S. 2016, MNRAS, 457, 4340 [NASA ADS] [CrossRef] [Google Scholar]
 Lahav, O., Lilje, P. B., Primack, J. R., & Rees, M. J. 1991, MNRAS, 251, 128 [NASA ADS] [CrossRef] [Google Scholar]
 Landy, S. D., & Szalay, A. S. 1993, ApJ, 412, 64 [NASA ADS] [CrossRef] [Google Scholar]
 Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, ArXiv eprint [arXiv:1110.3193] [Google Scholar]
 Le Fèvre, O., Saisse, M., Mancini, D., et al. 2003, in Proc. SPIE, eds. M. Iye, & A. F. M. Moorwood, 4841, 1670 [Google Scholar]
 Linder, E. V., & Cahn, R. N. 2007, Astropart. Phys., 28, 481 [NASA ADS] [CrossRef] [Google Scholar]
 Marulli, F., Bianchi, D., Branchini, E., et al. 2012, MNRAS, 426, 2566 [NASA ADS] [CrossRef] [Google Scholar]
 Mohammad, F. G., de la Torre, S., Bianchi, D., Guzzo, L., & Peacock, J. A. 2016, MNRAS, 458, 1948 [NASA ADS] [CrossRef] [Google Scholar]
 Okumura, T., & Jing, Y. P. 2011, ApJ, 726, 5 [NASA ADS] [CrossRef] [Google Scholar]
 Okumura, T., Hikage, C., Totani, T., et al. 2016, PASJ, 68, 38 [NASA ADS] [Google Scholar]
 Peacock, J. A., & Dodds, S. J. 1994, MNRAS, 267, 1020 [NASA ADS] [Google Scholar]
 Peacock, J. A., Cole, S., Norberg, P., et al. 2001, Nature, 410, 169 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 Peebles, P. J. E. 1980, The Largescale Structure of the Universe (Princeton University Press), 435 [Google Scholar]
 Percival, W. J., Ross, A. J., Sánchez, A. G., et al. 2014, MNRAS, 439, 2531 [NASA ADS] [CrossRef] [Google Scholar]
 Perenon, L., Piazza, F., Marinoni, C., & Hui, L. 2015, JCAP, 1511, 029 [NASA ADS] [CrossRef] [Google Scholar]
 Perenon, L., Marinoni, C., & Piazza, F. 2017, JCAP, 01, 035 [NASA ADS] [CrossRef] [Google Scholar]
 Perlmutter, S., Aldering, G., Goldhaber, G., et al. 1999, ApJ, 517, 565 [NASA ADS] [CrossRef] [Google Scholar]
 Piazza, F., Steigerwald, H., & Marinoni, C. 2014, JCAP, 1405, 043 [NASA ADS] [CrossRef] [Google Scholar]
 Planck Collaboration XIII. 2016, A&A, 594, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Reid, B. A., Samushia, L., White, M., et al. 2012, MNRAS, 426, 2719 [NASA ADS] [CrossRef] [Google Scholar]
 Reid, B. A., Seo, H.J., Leauthaud, A., Tinker, J. L., & White, M. 2014, MNRAS, 444, 476 [NASA ADS] [CrossRef] [Google Scholar]
 Riess, A. G., Filippenko, A. V., Challis, P., et al. 1998, AJ, 116, 1009 [NASA ADS] [CrossRef] [Google Scholar]
 Rota, S., Granett, B. R., Bel, J., et al. 2017, A&A, 601, A144 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Samushia, L., Percival, W. J., & Raccanelli, A. 2012, MNRAS, 420, 2102 [NASA ADS] [CrossRef] [Google Scholar]
 Sanchez, A. G., Scoccimarro, R., Crocce, M., et al. 2017, MNRAS, 464, 1640 [NASA ADS] [CrossRef] [Google Scholar]
 Satpathy, S., Alam, S., Ho, S., et al. 2017, MNRAS, 469, 1369 [NASA ADS] [CrossRef] [Google Scholar]
 Scoccimarro, R. 2004, Phys. Rev. D, 70, 083007 [NASA ADS] [CrossRef] [Google Scholar]
 Scoccimarro, R., Couchman, H. M. P., & Frieman, J. A. 1999, ApJ, 517, 531 [NASA ADS] [CrossRef] [Google Scholar]
 Scodeggio, M., Franzetti, P., Garilli, B., Le Fèvre, O., & Guzzo, L. 2009, The Messenger, 135, 13 [NASA ADS] [Google Scholar]
 Scodeggio, M., Guzzo, L., Garilli, B., et al. 2017, A&A, in press, DOI: 10.1051/00046361/201630114 [Google Scholar]
 Takahashi, R., Sato, M., Nishimichi, T., Taruya, A., & Oguri, M. 2012, ApJ, 761, 152 [NASA ADS] [CrossRef] [Google Scholar]
 Taruya, A., Nishimichi, T., & Saito, S. 2010, Phys. Rev. D, 82, 063522 [NASA ADS] [CrossRef] [Google Scholar]
 Uhlemann, C., Kopp, M., & Haugg, T. 2015, Phys. Rev. D, 92, 063004 [NASA ADS] [CrossRef] [Google Scholar]
 Weinberg, D., Bard, D., Dawson, K., et al. 2013, ArXiv eprints [arXiv:1309.5380] [Google Scholar]
 Weinberg, S. 1989, Rev. Mod. Phys., 61, 1 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
All Tables
Values of the growth rate and related parameters in the two redshift subsamples obtained by fitting the monopole and quadrupole correlation functions over the range 5 h^{1} Mpc <s< 50 h^{1} Mpc, using the TNS model. Central values and 68% marginalised errors on σ_{12}, fσ_{8}, and bσ_{8} are reported.
All Figures
Fig. 1 Footprint of the VIPERS observations within the W1 (top) and W4 (bottom) fields, as reconstructed from the final galaxy sample. The VIMOS pointings and quadrants are indicated by black rectangles. Galaxies are colour coded according to their value of the target sampling rate (TSR: see Sect. 4), which can be considered as a proxy for the inverse of the projected galaxy density field. Empty rectangles correspond to failed quadrants, for which the spectroscopic mask insertion failed or was incorrect, leading to no collection of data. 

Open with DEXTER  
In the text 
Fig. 2 Redshift distribution of the final VIPERS galaxy sample. The distributions of redshifts collected separately within the two CFHTLS fields are plotted together with the combined distribution using different colours. The red and purple solid lines show the best fit using the analytic template in Eq. (2) and the predicted V_{max} profile of the combined redshift distribution, respectively. The peculiar distribution of the VIPERS galaxy sample differs from the typical expectation from a magnitudelimited sample. This deviation is the result of the colourcolour preselection adopted to reject most galaxies located at z< 0.5. 

Open with DEXTER  
In the text 
Fig. 3 Diagram of the slit layout of a typical VIMOS pointing of the VIPERS survey (W1P082 in this case) superimposed on the actual DSS finding chart. The open circles with the tiny horizontal slits mark the target objects. The vertical rectangles define the area where the resulting spectrum falls once the dispersing element (grism) is inserted. 

Open with DEXTER  
In the text 
Fig. 4 Top: angular correlation function measured from the VIPERS W1 mock samples. In order to enhance the signaltonoise ratio, we show only the mean over 153 realisations. The angular correlation function of the parent and selected sample are shown with a cyan and magenta line, respectively. The two dashed vertical lines indicate the typical angular size of the slits and the raw spectra. Bottom: the completeness function, extracted from the mean of the 153 W1 mock samples (magenta line), is shown. The corresponding quantity measured from the VIPERS dataset is shown with red circles. 

Open with DEXTER  
In the text 
Fig. 5 Twodimensional completeness function deprojected along the orthogonal coordinate axis RADec. The imprint of a single galaxy spectrum is visible in the plot as the almost zero rectangular region at small angular separation. This region corresponds to the typical area occupied by the projected spectrum of an observed source. The enhancement of clustering in the top left region of the plot is produced by the particular displacement along common columns of slits within a quadrant. The scale of the abscissa and the ordinates is very different. 

Open with DEXTER  
In the text 
Fig. 6 Optimising the correction for the Target Sampling Rate on largescales; the tests are based on the mean of 153 mock samples. Top: systematic error on the realspace twopoint correlation function introduced by the TSR (dotted line), confronted to the results of different strategies to estimate its local value and the corresponding weight (see text for details). Circular apertures with varying radius (r = 90, 70 and 50 arcsec), and a rectangular aperture 60 × 100 arcsec^{2} are compared. The dotdashed line also shows the result of using a weight based only on the quadrantaveraged TSR. Note that here the smallscale further correction based on Eq. (6) has not been applied yet. Bottom: corresponding scatter of the different corrections. To allow comparison with the systematic error, this is also reported, for the rectangular aperture, as the shaded area in the top panel. 

Open with DEXTER  
In the text 
Fig. 7 Impact of the TSR and the SSR on the radial profile of the VIPERS galaxy samples. In the bottom panel we plot the relative difference of the V_{max} fits to the redshift distribution after applying the correction to the same obtained from the observed histogram. Dashed, dotted, and solid lines give the results for W1, W4, and the combined measurement, respectively. The smoothed radial profile is estimated using the V_{max} method. While the TSR does not affect the redshift distribution, the SSR enhances the number counts at z> 0.95. 

Open with DEXTER  
In the text 
Fig. 8 Impact of the target selection effects and their correction on the amplitude of the monopole (left) and quadrupole (right) of the redshiftspace correlation function ξ(s,μ). Considering the mean over 153 mock samples, in the bottom panel we plot the fractional deviation of the multipoles measured with the observed sample from those obtained using the parent catalogue. 

Open with DEXTER  
In the text 
Fig. 9 Final measurements of the anisotropic redshiftspace correlation function, ξ(r_{p},π) from the final data of the VIPERS survey within the two redshift ranges indicated by the labels. Solid contours correspond to isocorrelation levels of 0.3, 0.5, 1, 2, 5. 

Open with DEXTER  
In the text 
Fig. 10 Correlation matrices for the combined monopolequadrupole data vector in the low (top) and high (bottom) redshift bin. Correlation matrices are computed as , where C is the covariance matrix estimated from a set of 153 independent mock samples. The bottom left and top right squares correspond to the autocovariance of the monopole s^{2}ξ^{(0)} and the quadrupole s^{2}ξ^{(2)}, respectively, while the remaining squares show the crosscovariance terms. The scales under consideration range from s_{min} = 5 h^{1} Mpc to s_{max} = 50 h^{1} Mpc (from left to right). 

Open with DEXTER  
In the text 
Fig. 11 Relative systematic error on the measurement of the linear growth rate from the mean of 153 mock samples, as a function of the minimum fitting scale s_{min}, using the three models discussed in the text. The maximum scales fitted s_{max} is always fixed at 50 h^{1} Mpc. Here the parent mock samples are used to focus on the intrinsic performances of the models. The filled symbols correspond to the use of a Lorentzian form for the nonlinear damping factor in the models, whereas dashed lines correspond to a Gaussian form. In the top panel the full model as described in the text has been used. In the bottom panel, symbols are as above, but σ_{8} (and thus the shape of the nonlinear density and velocity divergence power spectra) has been fixed to the cosmology of the mock samples to test the effectiveness of the RSD model under ideal conditions. In this case, reducing the degrees of freeedom does reduce the statistical error bars, but for the TNS model seems to introduce a positive systematic error of ~5%, this is also the case for s_{min} as large as 25 h^{1} Mpc. 

Open with DEXTER  
In the text 
Fig. 12 Comparison between the bestfit models for the monopole and quadrupole on the averaged parent mocks using different combinations of RSD models and damping factors. The fit uses all separations down to s_{min} = 5 h^{1} Mpc. The use of a Gaussian damping in the models clearly dramatically worsens the accuracy of the fit, in particular for the largescale quadrupole signal. 

Open with DEXTER  
In the text 
Fig. 13 Effect of redshift errors on the recovered monopole and quadrupole from the galaxy mocks, obtained by adding to the mock redshifts a random Gaussian deviate with dispersion equal to the rms redshift error of the VIPERS. 

Open with DEXTER  
In the text 
Fig. 14 Same as Fig. 11, but now including Gaussian redshift errors with dispersion equal to the rms value measured for the VIPERS data, added to the mock galaxy redshifts. Here the dashed lines correspond to the use of a Lorentzian damping only, which in Fig. 11 was found to perform at best. With redshift errors, this needs to be supplemented by a further Gaussian damping factor, with dispersion fixed to the above rms error value, to yield the values described by the filled symbols. 

Open with DEXTER  
In the text 
Fig. 15 Same as Fig. 14, but now using the fully realistic observed mocks, on which all observational effects (masks, SPOC selection, and redshift errors) were included. As before, error bars correspond to the error on the average of the 153 samples. 

Open with DEXTER  
In the text 
Fig. 16 Monopole and quadrupole of ξ(r_{p},π) for the two redshift subsamples of the final VIPERS dataset (solid points), together with the final bestfitting curves obtained using the TNS model, corresponding to the values reported in Table 2. The likelihood computation has used data down to s_{min} = 5 h^{1} Mpc, as indicated by the tests. Error bars are 1σ deviations and correspond to the dispersion of the mock measurements. Each of these is also shown as a faint background line. 

Open with DEXTER  
In the text 
Fig. 17 Left panels: measured values of fσ_{8} from the VIPERS survey in the two redshift bins, using the TNS model in its optimal setup that we derived in section 8 (double damping factor: free Lorentzian + fixed Gaussian), as a function of the minimum fitting scale s_{min}. The maximum fitting scale s_{max} is always fixed at 50 h^{1} Mpc. The shaded area gives the statistical error at each s_{min} for the TNS model, as derived from the mocks. Right panels: the same measurements performed on the average of the mocks, i.e. plotting the results of Fig. 15 but showing explicitly the recovered values of fσ_{8}, to ease comparison with the data results on the left. 

Open with DEXTER  
In the text 
Fig. 18 One and twodimensional posterior likelihood distribution of the derived parameters fσ_{8}, bσ_{8} and σ_{12} for the 0.5 <z< 0.7 redshift bin. This corresponds to the result of the analysis of VIPERS data using the dispersion model, the Scoccimarro model, the Taruya model, and s_{min} = 5h^{1} Mpc. The dark and lightshaded areas correspond to the 68% and 95% joint twoparameter confidence levels, respectively. The lower redshift sample shows comparable contours and shapes. 

Open with DEXTER  
In the text 
Fig. 19 Plot of fσ_{8} vs. redshift, showing the VIPERS results together with a compilation of recent measurements. The previous results from the VVDS (Guzzo et al. 2008), SDSSMGS (Howlett et al. 2015), SDSSLRG (Samushia et al. 2012), WiggleZ (Blake et al. 2012), BOSS (Reid et al. 2012), 6dFGS (Beutler et al. 2012), and FastSound (Okumura et al. 2016) surveys are shown with the different symbols (see inset). The solid curve and associated error correspond to the prediction for general relativity in a ΛCDM model set to Planck 2015 cosmological parameters (Planck Collaboration XIII 2016). 

Open with DEXTER  
In the text 