Mon. Not. R. Astron. Soc. 000, 1{7 (2002) Printed 25 December 2018 (MN LATEX style file v2.2) Stellar populations in superclusters of galaxies M. V. Costa-Duarte1;2? L. Sodr´eJr.1 and F. Durret3 1Instituto de Astronomia, Geof´ısica e Ci^enciasAtmosf´ericas, Universidade de S~aoPaulo, S~aoPaulo, Brazil 2LUTH, Observatoire de Paris, CNRS, Universite Paris Diderot, Place Jules Janssen, 92190 Meudon, France 3UPMC Universit´eParis 06, UMR 7095, Institut d'Astrophysique de Paris, 98bis Bd Arago, F-75014, Paris, France ABSTRACT A catalogue of superclusters of galaxies is used to investigate the influence of the supercluster environment on galaxy populations, considering galaxies brighter than Mr <-21+5log h. Empirical spectral synthesis techniques are applied to obtain the stellar population properties of galaxies which belong to superclusters and represen- tative values of stellar population parameters are attributed to each supercluster. We show that richer superclusters present denser environments and older stellar popu- lations. The galaxy populations of superclusters classified as filaments and pancakes are statistically similar, indicating that the morphology of superclusters does not have a significative influence on the stellar populations. Clusters of galaxies within super- clusters are also examined in order to evaluate the influence of the supercluster envi- ronment on their galaxy properties. Our results suggest that the environment affects galaxy properties but its influence should operate on scales of groups and clusters, more than on the scale of superclusters. Key words: Cosmology: Large-scale structure in the Universe - superclusters of galaxies - spectral synthesis: galaxies. 1 INTRODUCTION making possible a detailed study of a variety of superclus- ter properties. A detailed map of the Universe has been Large scale structures are an important tool to study the produced, showing, among others, the most prominent su- Universe, presenting several useful features to constrain the percluster known, the Sloan Great Wall (Gott et al. 2005). cosmology. Groups and clusters of galaxies represent two Besides, many efforts are being applied to provide deeper fundamental classes of objects in this scenario, providing galaxy surveys, such as zCOSMOS (Lilly et al. 2007) and information on galaxy evolution and structure formation. GOODS/VIMOS (Popesso et al. 2009). Deep surveys make Many cluster catalogues have been compiled, presenting possible the identification of structures beyond the local Uni- properties for hundreds or even thousands of objects (Abell verse, reaching z∼1-3 (Tanaka et al. 2001; Gal & Rubin 2004; et al. 1989; Koester et al. 2007; Wen et al. 2009). Beyond arXiv:1210.0455v1 [astro-ph.CO] 1 Oct 2012 Kuiper et al. 2012). group and cluster scales, the large scale structure is traced We have compiled a supercluster catalogue using the by the largest type of object known: superclusters of galax- SDSS/DR7 database (Costa-Duarte et al. 2011, hereafter ies, extending to tens of megaparsecs and presenting a vari- CD11). Our analysis has shown that superclusters classi- ety of morphologies. The first galaxy redshift surveys have fied as filaments tend to be richer and more luminous. Us- allowed the compilation of the first supercluster catalogues, ing mock lightcones (Croton et al. 2006) we also concluded presenting data for about a hundred structures (Zucca et that peculiar velocities do not represent a significative influ- al. 1993; Einasto et al. 1994). Since then, the completion of ence on supercluster morphologies. Einasto et al. (2011a,b) new surveys led to a new generation of catalogues. For ex- also used the SDSS/DR7 database to discuss the relation ample, the Las Campanas Galaxy Redshift survey (Shect- between morphology and evolution in these structures. Lu- man et al. 1996) and the 2-degree Field Galaxy Redshift parello et al. (2011) presented a detailed study of the future Survey (Colless et al. 2001), with tens to hundreds of thou- of superclusters, collapsing into future virialized structures. sands of galaxies, have produced catalogues with several Recently Einasto et al. (2012) have shown that the cluster hundreds of superclusters (Einasto et al. 2007a). Nowadays, population in superclusters depends on their richness, with the Sloan Digital Survey (SDSS, Abazajian et al. 2009) pro- richer clusters being preferentially located in the high den- vides a database with roughly one million galaxy spectra, sity regions of rich superclusters. It is well known that galaxy environment has an im- portant role on galaxy evolution, affecting galaxy morphol- ? e-mail: [email protected] ogy and stellar populations (Davis & Geller 1976; Dressler c 2002 RAS 2 M. V. Costa-Duarte L. Sodr´eJr. and F. Durret 1980; Whitmore, Gilmore & Jones 1993). High density envi- Table 1. Supercluster samples. For each threshold density we ronments are usually populated by early-type galaxies, pre- give the number of filaments (Nf ), the number of pancakes (Np), senting old stellar populations, while late-type galaxies, with the number of clusters in filaments (Ncf ), the number of clusters younger stellar populations, are mostly found in low density in pancakes (Ncp), and the number of galaxies (Ng). environments. Environmental processes may act even at very large scales (Mo et al. 1992; Mateus et al. 2007). Several threshold Nf Np Ncf Ncp Ng studies have shown this effect by considering the environ- D 436 444 74 59 39469 mental effect on colors, morphology and luminosity function 1 D 215 194 64 60 22817 of galaxies (see Croton et al. 2005; Park et al. 2007; Car- 2 ollo et al. 2012). Einasto et al. (2007b) have compared the galaxy population properties of rich and poor superclusters in the 2dFGRS by using color index and spectral features, Abazajian et al. 2009), containing 120013 galaxies brighter concluding that richer structures present a higher fraction than Mr <-21+5logh in the redshift range 0.040≤z≤0.115. of early-type galaxies than the poorer ones. They also show Here we present a brief description of our approach, refering that main galaxies of rich superclusters are more luminous the reader to CD11 for more details. than the main galaxies in poor structures. Recently, Lietzen We have adopted the density field method to find high et al. (2012) have shown that groups of galaxies belonging density regions in the galaxy distribution. The luminosity to superclusters evolve faster than groups in the field or in density at a point r is given by voids, presenting higher luminosities and higher fractions of X passive galaxies. D(r) = K(jr − rij; σ)LiWi(ri); (1) In this paper we investigate the connection between stel- i lar populations of galaxies and supercluster properties. We where K(r; σ) is the Epanechnikov kernel, Li is the lumi- have obtained parameters useful to characterize the stellar nosity of the i-th galaxy within the kernel radius, and Wi is population of galaxies using empirical spectral synthesis of a statistical weight taking into account the selection effects. −1 SDSS spectra obtained with the STARLIGHT code of Cid The density field grid has cells with length lcel=4 h Mpc Fernandes et al. (2005). This technique aims at reproducing and the kernel smoothing parameter is σ=8 h−1Mpc. The an observed galaxy spectrum with a linear combination of luminosity density, which is used to find superclusters, is simple stellar populations (SSPs), taking into account ex- also useful for characterizing the local galaxy environment tinction and line broadening due to the stellar velocity dis- in investigations of stellar populations (see next Section). persion. It allows to estimate physical properties of galax- To evaluate the influence of the threshold density on the ies, such as star formation and chemical enrichment histories supercluster identification, our analysis has been performed and mean stellar ages and metallicities (e.g. Cid Fernandes with two thresholds, D1 = 3:0 × D0 and D2 = 6:0 × D0, et al. 2004; Tojeiro et al. 2007). where D0 is the mean luminosity density. The first threshold This paper is organized as follows. In Section 2 we maximizes the number of structures and the second is based present the data analyzed here: the supercluster catalogue on the Einasto et al. (2007a) criterion, assuming that the and the results of the spectral synthesis of supercluster largest supercluster has length ∼120h−1Mpc. galaxies. In Section 3 we discuss the connection between The morphology of superclusters was determined with stellar population parameters and supercluster properties, Minkowski Functionals. We have considered the parameters as well as that between galaxy clusters and superclusters. of planarity (K1) and filamentarity (K2) to classify super- Finally, in Section 4, we summarize our main results and clusters as filaments (K1/K2 ≤1.0) or pancake-like objects discuss their main implications for our knowledge of large (K1/K2 >1.0)(Sahni, Sathyaprakash & Shandarin 1998). scale structures. This ratio increases monotonically from filaments to pan- cakes. The richness (R) and total luminosity (Ltot) have been measured for all superclusters in our catalogue (see 2 THE DATA CD11 for more details). Table 1 shows some properties of the supercluster sam- In this section we describe the supercluster catalogue ples associated to each threshold. This table presents the (CD11) and the supercluster parameters (morphology, rich- number of filamentary and pancake-like superclusters, the ness, total luminosity). We also present the quantities as- number of clusters (see section 3.2) within each morphologi- sociated to the stellar populations (mean stellar ages and cal group and the number of galaxies for each threshold. Ob- metallicities) and galaxy environment (local density), de- viously, some of these galaxies are in both samples: 10953 of scribing how they were obtained. them. The total number of galaxies in this analysis is 50422. We assume an standard ΛCDM cosmology, when nec- We have verified that, qualitatively, the results of the essary, with Ωm = 0:3, ΩΛ = 0:7, and Hubble parameter next sections summarized in Figures 1 to 4 are not affected −1 −1 −1 H0 = 100 h km s Mpc .