The galaxy environment in GAMA G3C groups using the Kilo Degree Survey Data Release 3
M. V. Costa-Duarte, M. Viola, A. Molino, K. Kuijken, L. Sodré Jr., M. Bilicki, M. M. Brouwer, H. Buddelmeijer, F. Getman, A. Grado, J. T. A. de Jong, G. V. Kleijn, N. Napolitano, E. Puddu, M. Radovich, M. Vakili
MMNRAS , 1–13 (2017) Preprint 13 April 2018 Compiled using MNRAS L A TEX style file v3.0
The galaxy environment in GAMA G3C groups using theKilo Degree Survey Data Release 3
M. V. Costa-Duarte , (cid:63) , M. Viola , A. Molino , K. Kuijken , L. Sodré Jr. ,M. Bilicki , , M. M. Brouwer H. Buddelmeijer , F. Getman , A. Grado ,J. T. A. de Jong , G. V. Kleijn , N. Napolitano , E. Puddu , M. Radovich , M. Vakili Instituto de Astronomia, Geofísica e Ciencias Atmosféricas, University of São Paulo, R. do Matão 1226, 05508-090 São Paulo, Brazil Leiden Observatory, Leiden University, P.O. Box 9513, NL-2300 RA, Leiden, The Netherlands National Centre for Nuclear Research, Astrophysics Division, P.O. Box 447, PL-90-950 Lodz, Poland INAF - Osservatorio Astronomico di Capodimonte, via Moiariello 16, 80131 Napoli, Italy INAF - Osservatorio Astronomico di Padova, via dell’Osservatorio 5, 35122 Padova, Italy Kapteyn Astronomical Institute, University of Groningen, Postbus 800, 9700 AV, Groningen, The Netherlands
Accepted XXX. Received YYY; in original form ZZZ
ABSTRACT
We aim to investigate the galaxy environment in GAMA Galaxy Groups Catalogue(G3C) using a volume-limited galaxy sample from the Kilo Degree Survey Data Release3. The k-Nearest Neighbour technique is adapted to take into account the probabilitydensity functions (PDFs) of photometric redshifts in our calculations. This algorithmwas tested on simulated KiDS tiles, showing its capability of recovering the relationbetween galaxy colour, luminosity and local environment. The characterization of thegalaxy environment in G3C groups shows systematically steeper density contrasts formore massive groups. The red galaxy fraction gradients in these groups is evidentfor most of group mass bins. The density contrast of red galaxies is systematicallyhigher at group centers when compared to blue galaxy ones. In addition, distinctgroup center definitions are used to show that our results are insensitive to centerdefinitions. These results confirm the galaxy evolution scenario which environmentalmechanisms are responsible for a slow quenching process as galaxies fall into groupsand clusters, resulting in a smooth observed colour gradients in galaxy systems.
Key words: galaxy evolution – large-scale structure – photometric redshift – galaxyenvironment
The hierarchical structure formation theory predicts thatthe primordial density field in the early Universe evolvesthrough gravitational instabilities and its final stage is rep-resented by virialized dark matter dominated haloes. Thesesystems also represent potential wells for the baryonic mat-ter, which is gravitationally trapped, allowing galaxies toform (White & Rees 1978). Additionally, galaxies tend tocluster into larger structures and form the so-called cos-mic web (Vogeley et al. 2004; Gott III et al. 2005). Sev-eral works have shown that the environment within galaxysystems is essentially responsible for the galaxy quench-ing. Red galaxies are more often found in the densest re-gions of triplets, groups, clusters (Tempel et al. 2012; Costa- (cid:63)
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Duarte et al. 2016) and superclusters of galaxies (Einastoet al. 2011; Costa-Duarte et al. 2013; Einasto et al. 2014).From an observational point of view, some galaxy proper-ties are strongly correlated to their local environment, suchas colours, stellar population ages and morphology. It canbe observed in situ in the local Universe and its conse-quences are the morphology-density and colour-density re-lations (Dressler 1980; Goto et al. 2003; Kauffmann et al.2004; Dressler et al. 2013). In the colour-magnitude diagram(CMD), the mean colour of galaxies is independent of envi-ronment, but the red galaxy fraction increases as the localdensity increases at fixed luminosity (Balogh et al. 2004a;Ball et al. 2008). The galaxy colours seem to be more corre-lated to the environment than the morphology (Kauffmannet al. 2004; Quintero et al. 2006; Martinez & Muriel 2006),indicating that the morphological transformation is a sub-sequent and slower process. c (cid:13) a r X i v : . [ a s t r o - ph . GA ] A p r The galaxy-galaxy and galaxy-cluster interactions arethe main responsible mechanisms for the observed star for-mation quenching over a wide redshift range. Several mech-anisms are candidates for the galaxy quenching in distinctregions of galaxy clusters, such as merging (Icke 1985; Mi-hos 1995), harassment episodes (Moore et al. 1996, 1999),strangulation (Larson et al. 1980; Bekki et al. 2002) andram-pressure (Gunn & Got 1972). The role and contribu-tion of each environmental mechanisms for the quenchingprocess and stellar mass build-up still remains unclear (e.g.,Capak et al. 2007; van der Wel et al. 2010; Rowlands etal. 2018). This current scenario suggests a slow gas removalfrom late-type galaxy haloes with no observed structuralchanges, with galaxies becoming quiescent due to the lackof gas reservoir for star formation but keeping their mor-phology still disk-like. Afterwards, a morphological trans-formation takes place due to more significant gravitationalinteractions at inner cluster regions and finally elliptical andred galaxies (so-called red and dead ) mostly populate centralregions of galaxy clusters (Balogh et al. 1998).Beyond the local Universe (z ∼ ∼ IMAFLAG_ISO indicates if the photometry is contaminatedby observational issues (bad pixels, cosmic rays, saturatedstars, etc). The galaxy sample used in this work is ex-tracted from the multi-band catalog, with =0 and IMAFLAG_ISO_r =0. These flags mean that objects are reli-ably classified as galaxies and have accurate photometry.The MAG_AUTO magnitudes are corrected by the Galactic ex-tinction and homogenized using the zero-point offsets pro-vided for each photometric band and tile . Because of the http://kids.strw.leidenuniv.nl/DR3/format.phpMNRAS , 1–13 (2017) he galaxy environment in GAMA G3C groups using the Kilo Degree Survey Data Release 3 Figure 1. The volume-limited sample extracted from theKiDS/DR3 database. The dashed line represents the luminos-ity threshold imposed to the sample. The high-density regions atz ∼ photometric depth at r band slightly varies among KiDStiles, a conservative magnitude limit is adopted by select-ing objects brighter than r =22.5. The package kcorrect v4.2 (Blanton et al. 2007) is employed to obtain the rest-frame magnitudes using the photo-z provided by the BPZ code (Benitez 2000). We extract volume-limited galaxy sam-ples from each KiDS/DR3 tile, taking objects brighter than M r < -19.3 and within the redshift range 0.01 To evaluate the capability of the proposed galaxy environ-ment algorithm (see section 3), we generate KiDS mock cat-alogs. The photo-z modelling on the KiDS mock catalogconsists of two parts. The behaviour of the photo-zs as afunction of: i) the apparent magnitude ( r band), and ii) thegalaxy colour ( g − r ) . The first one is obtained from Fig.11 of Kuijken et al. (2015)(hereafter K15), using the rela-tion between σ z ≡ std ( δ z / (1 + z spec )) and the apparent r band, where δ z = ( z phot − z spec ) . The second one requiresspectroscopic data to evaluate the photo-z uncertainties asa function of galaxy colours. The spectroscopic GAMA sur-vey (Driver et al. 2009) overlaps with the KiDS coveragefor four fields (G09, G15, G12 and G23), however, present-ing a shallower sample ( r< σ z ( r, g − r ) ) up to r =19.8. For fainterobjects, the relation presented by K15 is adopted. Figure 2shows the photo-z uncertainties as a function of the r -bandand galaxy colour ( g − r ) for the bright side and the relationfrom K15 for fainter objects. The representative photo-z un-certainty is obtained as the mean value over the apparentmagnitude range, being 0.042(1+ z ).Our KiDS/DR3 sample also suffers from photo-z out-liers. Particularly, high redshift galaxies (0.4 The redshift uncertainties ( σ z ) as a function of r -bandand ( g − r ) . The blue continuous line is σ z as a function of r band, following the photo-z uncertainty behaviour from Kuijkenet al.(2015). Dashed lines represent the ( g − r ) -dependent redshiftuncertainties obtained from the match between GAMA and KiDSsurveys. Lower dashed parallel lines represent redder galaxies. magnitudes and galaxies can be wrongly included or ex-cluded from our volume-limited sample, according to theirphoto-z uncertainties. Overestimated photo-zs can includegalaxies which are fainter than -19.3 into our volume-limitedsample, being an incoming. On the other hand, underesti-mated photo-zs can exclude objects brighter than the abso-lute magnitude limit from the sample, i.e., outgoing. Figure3 shows the mock volume-limited sample extracted usingphoto-zs (upper panel) and the same sample by consideringabsolute magnitudes from the spec-zs (middle panel). In thelower panel, we show the incoming and outgoing as a func-tion of the photo-z. The incoming presents constant values(around 7%) and decreases as it gets closer to the redshiftlimit (z b =0.4). The incoming reaches null value at the upperredshift limit due to the magnitude cut r =22.5. It excludesall objects fainter then -19.3 at the redshift limit then theincoming is null at this redshift by definition. The incom-ing is null at the lower redshift range since it comes fromunderestimated photo-zs. The outgoing roughly increaseswith photo-z due to the photo-z uncertainty being larger athigher photo-zs. The contamination parameters are furtheremployed to correct the local density (see Section 3). The GAMA project (Data Release 2, Liske et al. 2015)is an extragalactic multiwavelength survey which com-bines photometry (far-UV to radio) and optical spectra ofmore than 290000 objects over 286 sq.deg. The opticalspectroscopy employs the AAOmega spectrograph on theAnglo-Australian Telescope (AAT). The aperture matchedphotometry provides optical SDSS petrosian magnitudes( ugriz ) and infrared bands ( Y JHK ) from the VIKING sur-vey (Edge et al. 2013) for targets down to r AB =19.8. Animpressive spectroscopic completeness ( ∼ friends-of-friends (FoF) algorithm was adapted to take into account the se-lection function of the survey to identify galaxy systems upto z< log M h (parameter MassA )were estimated from a dynamical proxy, using the groupvelocity dispersion ( σ group ) and the projected radius whichcontains 50% of the members ( R ) as well as the scaling fac-tor A (for more details, see Robotham et al. 2011). Galaxygroups more massive than log M h = Y JHK ) from the VIKING sur-vey (Edge et al. 2013) for targets down to r AB =19.8. Animpressive spectroscopic completeness ( ∼ friends-of-friends (FoF) algorithm was adapted to take into account the se-lection function of the survey to identify galaxy systems upto z< log M h (parameter MassA )were estimated from a dynamical proxy, using the groupvelocity dispersion ( σ group ) and the projected radius whichcontains 50% of the members ( R ) as well as the scaling fac-tor A (for more details, see Robotham et al. 2011). Galaxygroups more massive than log M h = Simulated volume-limited sample and the contamination due to photo-z uncertainties. Upper: sample initially constrainedusing the absolute magnitudes calculated from photo-zs. Middle: galaxy sample from the upper panel but showing absolute magnitudescalculated using spec-zs. Lower: Contamination fraction of galaxies as function of the redshift. Solid and dashed lines represent theincoming and outgoing over all simulated KiDS tiles, respectively. concept of neighbours is now adapted in a probabilistic for-malism. The probability of the i-th galaxy of being in theredshift range is, P i = (cid:90) z +∆ z (1+ z ) z − ∆ z (1+ z ) P DF ( z ) dz. (2)The local density of galaxies with the inclusion of PDFs canbe written as, σ ( R , z ) = S k πR . (3)where S k is the sum of P i over all galaxies enclosed by R kNN .The projected radius R kNN increases until the probabilitysum of all neighbour candidates reaches the desired value k ( S k = k ) in contrast to solely galaxy counting, as shown inthe initial technique.The evaluation of both algorithms described above inthe mock catalogs is carried out using the k-NN techniquein spec-z space. In this case, the local densities are esti-mated using spheres with radius r KNN which contain k near-est neighbours. The spectroscopic local density is then de-fined as ρ spec ( R , z ) = k ( πr ) . This volumetric densityis considered as the reference galaxy environment. Althoughthe galaxy environment densities calculated in spec-z andphoto-z are not the same by definition, we are able to finda positive correlation between them. The galaxy environment formalism presented above is stillaffected by contamination, masking and border effects. Somegalaxies located at the tile border can have their local den-sity underestimated due to the non-continuity of the survey.The correction needed to this missing area is defined as thefraction of the circle projected in the sky with radius R kNN situated outside of the survey boundaries or affected by badpixels, i.e., f area = A out /πR . This method assumes thatthe area outside the circle presents the same local density ofgalaxies obtained within the survey area. The area correc-tion weight is then defined as w area = 1 / (1 − f area ) and itincreases as the missing area fraction increases. If there is nomissing area, f area = 0 and consequently w area = 1 . A simi-lar correction is also necessary due to redshift limits of thesample. The individual redshift ranges for each galaxy cellpreviously defined can have part of its volume outside theredshift range of the galaxy sample and their local densitiescan be again underestimated. The redshift correction sim-ply consists of the volume fraction outside the survey, f z = V out /V cell and similarly w z = 1 / (1 − f z ) . The sample contam-ination described in the subsection 2.2 is corrected by usinga similar formalism: w C ( z ) = (1 − f C ( z )) , where f C ( z ) is thedifference between the incoming and outgoing as a functionof the photo-z, i.e., f C ( z ) = C incoming ( z ) − C outgoing ( z ) . If C incoming is larger/smaller than C outgoing , w C is lower/higher MNRAS , 1–13 (2017) than unity. This correction takes into account the galaxycontamination in our volume-limited sample.The local density of galaxies is simultaneously correctedby sky area, volume and contamination, i.e., σ corr ( R , z ) = σ ( R , z ) w area w z w C . The volume and area corrections areessentially geometrical and are applied to spectroscopic andphoto-z samples following their individual geometry, accord-ingly.As our observed and simulated data is configured intiles, the tile management is mandatory for the calculationsin order to reduce the border effect and consequently increas-ing the sky continuity. Some of the tiles have neighbouringtiles around them (see Figure 1 from de Jong et al. 2017)while others are basically isolated. Tiles which have oth-ers nearby form a larger contiguous area and consequentlythese close tiles are included in the calculations to maxi-mize the continuity of the sky area. The galaxy environmentfor isolated tiles are simply calculated without the inclusionof other tiles. It means that the border corrections are fre-quently applied for these tiles. The local density of galaxies is regularly transformed to den-sity contrast in the literature in order to make compara-ble different galaxy environment techniques or parametriza-tions. Hereafter, the local density of galaxies is converted todensity contrast as follows, δ = σ ¯ σ , (4)where σ is the local density and ¯ σ represents the averagedensity.Since the galaxy environment technique previously pre-sented is parametrized as a function of the number of neigh-bours, we adopt values from k =2 to k =50. The k-NN tech-nique traces galaxy environments at large scales for largevalues of k, with the environmental scale proportional tothe value of k.The Spearman correlation coefficient evaluates possi-ble correlation between the density contrasts in the spec-z and photo-z spaces from the simulated KiDS/DR3 tiles.This coefficient r s varies between -1 and +1, indicating anti-correlation and correlation between two sets, respectively.The null hypothesis probability ( P ( H ) ) says how proba-ble these two sets of data are correlated. It also indicateswhether r s is statistically significant or not, preferred tobe lower than − or < σ for a significant correlation.Figure 4 (left) shows the density contrast comparison be-tween the spectroscopic ( log (1 + δ spec ) ) and photometric( log (1 + δ phot ) ) redshift spaces. This result indicates thatthe galaxy environment can be estimated in the KiDS sur-vey using the technique presented in the Section 3. Positivecorrelations are found for both approaches (the inclusionor not of PDFs) and several numbers of neighbours. Notethat the relation between the density contrasts is not cen-tred on the 1:1 line. The density field of galaxies followsroughly a log-normal distribution, and any other normalisa-tion which does not use the median value would not bring both distributions centred at (0,0). As our analysis is pre-sented comparatively, it should not affect our results. Figure4 (right) also shows the Spearman correlation coefficient asa function of the number of neighbours with and withoutthe PDF inclusion in our calculations. The correlation co-efficient peaks at ( r s , P(H ))=(0.42, < − ) and k =5, anddecreases as the number of neighbours increases. We choose k =5 for our further analysis in this paper. It seems thatlow number of neighbours ( k< 5) is more susceptible to red-shift uncertainties due to the low countings. At larger scales( k> The galaxy environment technique described in Section 3is applied on all tiles in KiDS/DR3 database, covering thenumber of neighbours from k =2 to k =50. However, furtherresults are shown only for k =5, presenting a relatively highercorrelation coefficient. The KiDS/DR3 density contrasts arethen divided into quartiles of sources according to their den-sity contrasts: log (1+ δ ) ≤ -0.25, -0.25 < log (1+ δ ) ≤ -0.11, -0.11 < log (1+ δ ) ≤ +0.05 and log (1+ δ ) > +0.05. These binsare chosen in order to have a significant number of objectsin all density contrast bins and roughly separate galaxiesinto low density, mean density, overdensity and high densityenvironments.The galaxy classification between red and blue isadopted in further analysis using a ( g − r ) limit presentedby Cooper et al. (2010) (C10) to define the blue limit of thered sequence, ( g − r ) C = − . M r + 0 . , (5)where M r is the absolute magnitude at r band. Objectsabove or below this colour and luminosity thresholds areclassified as red or blue galaxies, respectively.Figure 5 shows the galaxy colour ( g − r ) histogramsat the rest-frame in absolute magnitude ( M r ) and envi-ronment contrast ( log (1 + δ ) ) bins between 0.01 Results for eight simulated KiDS tiles. Left: Correlation between the density contrast dereived from spec-z and photo-z usingthe PDFs for k =5. Right: Spearman correlation coefficient as a function of the parameter k , i.e., number of the neighbours (see Section 3).Solid/dashed lines represent the k-NN technique with/without the inclusion of the reconstructed PDFs in the calculations, respectively. Since the G3C groups have been identified from amagnitude-limited sample, any galaxy population analysiswould demand a strong selection function correction by us-ing only GAMA data. The KiDS/DR3 volume-limited sam-ple is then suitable to carry out a homogeneous analysis ofthe group sample, keeping the same selection function (orluminosity threshold of galaxies) over all galaxy systems.Our analysis considers GAMA galaxies in G3C groups( r <19.8) and on their outskirts (up to twice the radius thatcontains 100% of all group members, i.e., 2 R ) within thegroup velocity dispersion ( σ group ), previously calculated byRobotham et al. (2011). KiDS galaxies around groups areextracted from the KiDS volume-limited sample followingthe redshift uncertainty of the photo-zs, selecting galaxies inthe redshift range z group ± . z group ) within R ≤ R around the structure center. In summary, this analysis con-sists of combining the shallower spectroscopic sample fromGAMA and a deeper and volume-limited samples from theKiDS database in order to keep the homogeneity of galaxypopulation in all groups within the redshift range. Essen-tially, it considerably increases the redshift range of ouranalysis which would be much smaller if we only considerspectroscopic data. The galaxy environment is evaluated in G3C groups as afunction of the normalised group radius (R/R ) and abso-lute magnitude bins. Figure 6 shows the density contrast asa function of the normalised radius compared to the centralvalues of G3C groups for galaxy luminosity bins and differ-ent group mass ranges. The median gradient for the lowestmass group bin has values of +0.4 dex at central cores and+0.1 dex at outer regions (R/R > Figure 7 shows the fraction of red galaxies as a function ofthe normalised radius of galaxy groups for galaxy luminos-ity and group mass bins. The red galaxy fraction clearly de-creases as a function of the normalised radius for most cases,as expected. Due to photo-z uncertainties, there is no signifi-cant difference between these relations, unless for the bright-est galaxy luminosity bin, between the lowest and highestgroup mass bins. The faintest galaxies ( M r > -20) present redgalaxy fractions around 0.5 at group cores and decrease onthe outskirts, reaching values around 0.3. These radial gradi-ents become redder and more prominent for more luminousbin. For the next two luminosity bins (-21< Figure 5. Normalised histograms of rest-frame colour ( g − r ) . for the KiDS/DR3 volume-limited sample between 0.01 MNRAS000 MNRAS000 , 1–13 (2017) he galaxy environment in GAMA G3C groups using the Kilo Degree Survey Data Release 3 Figure 6. The density contrast ( log (1 + δ ) ) as a function of the normalised radius of G3C groups ( R/R ). The luminosity bins M1,M2, M3 and M4 are represented by blue, green, red and black lines, respectively. The mean gradients of increasing G3C halo mass binsare represented by the panels from (a) to (c), respectively. The vertical and horizontal lines represent the normalised radius of the group(R/R =1) and the median density of the KiDS/DR3 galaxy sample, respectively. The shaded area represents 1 σ uncertainties and thedashed line represents the one unit of group radius. Figure 7. The red galaxy fraction as a function of the normalised group radius (R/R ) is shown for all luminosity bins. The red galaxyfraction gradients are shown for different G3C group mass bins. Shaded areas represent 1 σ dispersion. The density contrast distributions of galaxies classified as red (solid line) and blue (dashed line) shown in group radius bins andgroup mass bins. The radius bins are defined as 0.5 R/R wide, up twice the normalised radius. The solid and dashed lines representthe red and blue galaxy distributions, respectively. The fraction of red and blue galaxies are shown at the upper region of all panels. One of the consequences of combining spec-z and photo-z inour analysis is the projection effects. The gradient analysisfrom the galaxy groups shown here is based on the projectedsky plane. Group members close to the group centers in the2D sky plane can actually be background or foreground ob-jects in the redshift range z group ± . z group ) . Dueto the photo-z uncertainties, it is not possible to deprojectthese objects. Our gradients are calculated using galaxies inthe sky plane and within z group ± . z group ) . This pro-jection effect systematically decreases the discrepancies be-tween the galaxy populations and the density contrast gra-dients due to background and foreground contaminations.On the other hand, this projection effect is homogeneousover all G3C groups since our gradients follow the redshiftuncertainties of KiDS galaxies over all redshift bins, keepingthe same background contamination throughout the redshiftrange.Having this limitation in mind, we also analysed galaxyhaloes above log ( M FoF /M (cid:12) ) = 13 in our mock catalogue(see Section 2.2) by using the same criteria of group gradi-ents aforementioned. The results indicated that the fractionof halo members recovered and the contamination of fore- ground and background galaxies due to photo-z uncertain-ties is 67.3% and 48.4% for the KiDS photo-z uncertainties,respectively.In addition, comparing the fraction of red galaxies withother works in the literature, our red galaxy fraction is sim-ilar to those found by van der Wel et al. (2010) (see theirFigure 1) at central regions of rich clusters ( log ( M h ) ∼ ),between 0.6 and 0.8. It is important to mention that we areaware of projected galaxies in the line-of-sight due to photo-zuncertainties and its effects, however, our results are alwaysshown in a comparative way, separating the galaxy sampleinto luminosity, distance from the group center and groupmasses. We initially adopt the BCGs as centers of G3C groups (Sec-tion 2.3), as previously shown. Nonetheless, the GAMA G3Ccatalog also provides other group center definitions for thegalaxy systems. Thus, we evaluate the influence of a sec-ond center definition in our results. The r -band luminosity-weighted center is then employed to evaluate how sensitivethe density contrast distributions of red and blue galax-ies are to the center definition. The Appendix A illustrates MNRAS000 One of the consequences of combining spec-z and photo-z inour analysis is the projection effects. The gradient analysisfrom the galaxy groups shown here is based on the projectedsky plane. Group members close to the group centers in the2D sky plane can actually be background or foreground ob-jects in the redshift range z group ± . z group ) . Dueto the photo-z uncertainties, it is not possible to deprojectthese objects. Our gradients are calculated using galaxies inthe sky plane and within z group ± . z group ) . This pro-jection effect systematically decreases the discrepancies be-tween the galaxy populations and the density contrast gra-dients due to background and foreground contaminations.On the other hand, this projection effect is homogeneousover all G3C groups since our gradients follow the redshiftuncertainties of KiDS galaxies over all redshift bins, keepingthe same background contamination throughout the redshiftrange.Having this limitation in mind, we also analysed galaxyhaloes above log ( M FoF /M (cid:12) ) = 13 in our mock catalogue(see Section 2.2) by using the same criteria of group gradi-ents aforementioned. The results indicated that the fractionof halo members recovered and the contamination of fore- ground and background galaxies due to photo-z uncertain-ties is 67.3% and 48.4% for the KiDS photo-z uncertainties,respectively.In addition, comparing the fraction of red galaxies withother works in the literature, our red galaxy fraction is sim-ilar to those found by van der Wel et al. (2010) (see theirFigure 1) at central regions of rich clusters ( log ( M h ) ∼ ),between 0.6 and 0.8. It is important to mention that we areaware of projected galaxies in the line-of-sight due to photo-zuncertainties and its effects, however, our results are alwaysshown in a comparative way, separating the galaxy sampleinto luminosity, distance from the group center and groupmasses. We initially adopt the BCGs as centers of G3C groups (Sec-tion 2.3), as previously shown. Nonetheless, the GAMA G3Ccatalog also provides other group center definitions for thegalaxy systems. Thus, we evaluate the influence of a sec-ond center definition in our results. The r -band luminosity-weighted center is then employed to evaluate how sensitivethe density contrast distributions of red and blue galax-ies are to the center definition. The Appendix A illustrates MNRAS000 , 1–13 (2017) he galaxy environment in GAMA G3C groups using the Kilo Degree Survey Data Release 3 the same analysis as shown in Figure 8 but now using the r -band luminosity-weighted group centers. We notice thatthe red/blue fractions and the red and blue histograms aresimilar to the ones calculated using the BCG as definitionof group center. Consequently, our conclusions are insensi-tive to the new center definition. The local density excessfound on the outskirts of low mass systems is still found at log (1 + δ ) > | r L - r BCG | ) is ∼ − Mpc, corresponding to 12% of the aver-age R for our group sample. This relatively small offsetindicates that the group center definitions for G3C groupsare quite stable and do not change our previous conclusions. We investigated the galaxy environment in GAMA G3Cgroups using a volume-limited galaxy sample ( M r < -19.3 and0.01 < z < log( M h ) > . .Our main findings are the following: • The simulated KiDS/DR3 tiles showed the capability ofthe adapted k-NN technique to recover the galaxy environ-ment in the KiDS/DR3 database. We were able to recoverthe relation between the galaxy environment, luminosity andthe galaxy colour ( g − r ) up to z =0.4. • Using the KiDS galaxy sample, we evaluated the galaxypopulation in these galaxy systems and on their outskirts.Density contrast gradients were systematically steeper formore massive systems, reaching on average +0.6 dex higherthan their outskirts. • We separated the galaxy population into two mainclasses, blue and red ones using a colour-magnitude cutadopted by Cooper et al. (2010). The fraction of red galaxiesas a function of the normalised radius ( R/R ) presents,for the faintest galaxies, ∼ 50% of red galaxies and de-creases as the radius increases. As the luminosity increases,it reaches ∼ 80% at group centers and decreases on the out-skirts. Higher dispersion is noticed for the most luminousbin, probably due to the low number of galaxies. • The density contrast distribution for red galaxiesshowed an excess of high density regions when compared tothe blue galaxies at the center of groups ( R/R < R/R > • The influence of the group center definition on our re-sults is also evaluated. First, the brightest cluster galaxy as center definition is employed for our main conclusions. Us-ing the r-band luminosity weighted center as a new centerdefinition, similar conclusions pointed out the insensitivityof the center definition in our analysis.Several mechanisms can be responsible for the galaxyquenching found in this work, acting on galaxies at differentdistances from the group center, such as merging (Icke 1985;Mihos 1995) and harassment (Moore et al. 1996, 1999) overall scales, and ram-pressure (Gunn & Got 1972) and tidal-stripping (Nulsen 1982; Toniazzo & Schindler 2001) at theinner regions. The correlation between the fraction of redgalaxies and the local density was previously found in theliterature, being lower fractions for fainter galaxies (e.g. Ballet al. 2008). However, this result is not found here proba-bly due to the photo-z uncertainties of the KiDS database.The current quenching scenario predicts that hydrodynami-cal quenching mechanisms (e.g. ram-pressure) slowly removethe cold gas from galaxy halos and consequently quench theinfall galaxy. An abrupt and extreme quenching mechanism(mechanical ones, such as mergers or harassment) would per-turb the gas within the galaxy halo and then trigger thestar formation in these galaxies. As a consequence, it wouldreduce the fraction of the red galaxies. Hydrodynamical ef-fects are mainly responsible for the smoothly colour changesat the outer part of galaxy groups and clusters. The intra-cluster hot gas is the main candidate to carry out this hy-drodynamical quenching at that region. Recently, Zinger etal. (2016) used simulations to propose that the quenchingprocess starts much earlier, beyond the virial radius andits consequences are only observed 2-3 Gyrs after the ini-tial quenching. Another explanation can be the "splashback"galaxies. Having a highly excentric orbit, spiral galaxies ininfall process would rapidly pass through the inner virialradius of the cluster and lose their neutral hydrogen. Afterthat, they are already in quenching process and will spendmost of the time on the cluster outskirts (1-2.5 virial radii)due to their eccentric orbits (Mamon et al. 2004).At the inner parts, the mechanical processes are respon-sible for perturbing galaxies, often causing morphologicaltransformation (e.g. von der Linden et al. 2010). In sum-mary, there is no specific mechanism that fully explainsboth colour-environment and morphology-environment re-lations in galaxy clusters. They act all together in order toreproduce the observed transition from disky/star-forminggalaxies to spheroidal/passive ones (Park & Hwang 2009).The galaxy environment technique presented here canbe also applied on other galaxy surveys in the future, such asS-PLUS (Mendes de Oliveira et al., in preparation), J-PLUS(Cenarro et al., in preparation), J-PAS (Benitez et al. 2014)and EUCLID (Clémens et al. 2015). ACKNOWLEDGEMENTS MVCD thanks the financial support from FAPESP (pro-cesses 2014/18632-6 and 2016/05254-9) and the Univer-sity of Leiden, the Netherlands, for their hospitality. AMacknowledges the financial support of the Brazilian fund-ing agency FAPESP (Post-doc fellowship - process number2014/11806-9) MB is supported by the Netherlands Orga-nization for Scientific Research, NWO, through grant num-ber 614.001.451. This work has made use of the computing MNRAS , 1–13 (2017) facilities of the Laboratory of Astroinformatics (IAG/USP,NAT/Unicsul), whose purchase was made possible by theBrazilian agency FAPESP (grant 2009/54006-4) and theINCT-A. GVK acknowledges financial support from theNetherlands Research School for Astronomy (NOVA) andTarget. Target is supported by Samenwerkingsverband No-ord Nederland, European fund for regional development,Dutch Ministry of economic affairs, Pieken in de Delta,Provinces of Groningen and Drenthe. REFERENCES Allen, R. J., Kacprzak, G. 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V., Nagai, D., astro-ph:1610.02644 APPENDIX A: THE INFLUENCE OF THEGROUP CENTER DEFINTION Figure A1 shows the density contrast distributions of galax-ies classified as blue and red as a function of group mass andnormalised radius bins for the r-band luminosity weightedgroup center. The comparison between Figures A1 and 8indicates that the center definition does not change our con-clusions. Moreover, the conclusions obtained from Figures 6and 7 are not changed either. This paper has been typeset from a TEX/L A TEX file prepared bythe author.MNRAS , 1–13 (2017) Figure A1. The same as Figure 8 but using the r band luminosity weighted center defined by Robotham et al. (2011).MNRAS000