a r X i v :0704.0579v 1 [a s t r o -p h ] 4 A p r 2007
Astronomy &Astrophysics manuscript no.babbage c
ESO 2008February 1,2008
Substructures in WINGS clusters ?
M.Ramella 1,A.Biviano 1,A.Pisani 2,J.Varela 3,D.Bettoni 3,W.J.Couch 4,M.D’Onofrio 5,A.Dressler 6,G.Fasano 3,
P.Kj?rgaard 7,M.Moles 8,E.Pignatelli 3,and B.M.Poggianti 3
1INAF /Osservatorio Astronomico di Trieste,via G.B.Tiepolo 11,I-34131,Trieste,Italy
2Istituto di Istruzione Statale Classico Dante Alighieri,Scienti?co Duca degli Abruzzi,Magistrale S.Slataper,viale XX settembre 11,I-34170Gorizia,Italy
3
INAF /Osservatorio Astronomico di Padova,vicolo Osservatorio 5,I-35122,Padova,Italy 4School of Physics,University of New South Wales,Sydney 2052,Australia 5Dipartimento di Astronomia,Universit`a di Padova,vicolo Osservatorio 2,I-35122Padova,Italy 6Observatories of the Carnegie Institution of Washington,Pasadena,CA 91101,USA
7Copenhagen University Observatory.The Niels Bohr Institute for Astronomy Physics and Geophysics,Juliane Maries Vej 30,2100Copenhagen,Denmark 8
Instituto de Astrof′?sica de Andaluc′?a (C.S.I.C.)Apartado 3004,18080Granada,Spain
Received /Accepted
ABSTRACT
Aims.We search for and characterize substructures in the projected distribution of galaxies observed in the wide ?eld CCD images of the 77nearby clusters of the WIde-?eld Nearby Galaxy-cluster Survey (WINGS).This sample is complete in X-ray ?ux in the redshift range 0.04 Methods.We search for substructures in WINGS clusters with DEDICA,an adaptive-kernel procedure.We test the procedure on Monte-Carlo simulations of the observed frames and determine the reliability for the detected structures. Results.DEDICA identi?es at least one reliable structure in the ?eld of 55clusters.40of these clusters have a total of 69substructures at the same redshift of the cluster (redshift estimates of substructures are from color-magnitude diagrams).The fraction of clusters with subclusters (73%)is higher than in most studies.The presence of subclusters a ?ects the relative luminosities of the brightest cluster galaxies (BCGs).Down to L ～1011.2L ⊙,our observed di ?erential distribution of subcluster luminosities is consistent with the theoretical prediction of the di ?erential mass function of substructures in cosmological simulations.Key words.Galaxies:clusters:general –Galaxies:kinematics and dynamics 1.Introduction According to the current cosmological paradigm,large struc-tures in the Universe form hierarchically.Clusters of galaxies are the largest structures that have grown through mergers of smaller units and have achieved near dynamical equilibrium.In the hierarchical scenario,clusters are a rather young population,and we should be able to observe their formation process even at rather low redshifts.A signature of such process is the presence of cluster substructures.A cluster is said to contain substruc-tures (or subclusters)when its surface density is characterized by multiple,statistically signi?cant peaks on scales larger than the typical galaxy size,with “surface density”being referred to the cluster galaxies,the intra-cluster (IC)gas or the dark matter (DM hereafter;Buote 2002). Studying cluster substructure therefore allows us to investi-gate the process by which clusters form,constrain the cosmo-logical model of structure formation,and ultimately test the hi-erarchical paradigm itself (e.g.Richstone et al.1992;Mohr et al.1995;Thomas et al.1998).In addition,it also allows us to better understand the mechanisms a ?ecting galaxy evolution in clus-ters,which can be accelerated by the perturbative e ?ects of a cluster-subcluster collision and of the tidal ?eld experienced by 2M.Ramella et al.:Substructures in the WINGS clusters Roettiger et al.1997;Barrena et al.2002;Clowe et al.2006). Hence,it is equally useful to address cluster substructure analy-sis in the X-ray and in the optical. Traditionally,the?rst detections of cluster substructures were obtained from the projected spatial distributions of galax-ies(e.g.Shane&Wirtanen1954;Abell et al.1964),in com-bination,when possible,with the distribution of galaxy ve-locities(e.g.van den Bergh1960,1961;de Vaucouleurs1961). Increasingly sophisticated techniques for the detection and characterization of cluster substructures have been developed over the years(see Moles et al.1986;Perea et al.1986a,b; Buote2002;Girardi&Biviano2002,and references therein). In many of these techniques substructures are identi?ed as de-viations from symmetry in the spatial and/or velocity distri-bution of galaxies and in the X-ray surface-brightness(e.g. West et al.1988;Fitchett&Merritt1988;Mohr et al.1993; Schuecker et al.2001).In other techniques substructures are identi?ed as signi?cant peaks in the surface density distribu-tion of galaxies or in the X-ray surface brightness,either as residuals left after the subtraction of a smooth,regular model representation of the cluster(e.g.Neumann&B¨o hringer1997; Ettori et al.1998),or in a non-parametric way,e.g.by the tech-nique of wavelets(e.g.Escalera et al.1994;Slezak et al.1994; Biviano et al.1996)and by adaptive-kernel techniques(e.g. Kriessler&Beers1997;Bardelli et al.1998a,2001). The performances of several di?erent methods have been evaluated both using numerical simulations(e.g.Mohr et al. 1995;Crone et al.1996;Pinkney et al.1996;Buote&Xu 1997;Cen1997;Valdarnini et al.1999;Knebe&M¨u ller2000; Biviano et al.2006)and also by applying di?erent methods to the same cluster data-sets and examine the result di?er-ences(e.g.Escalera et al.1992,1994;Mohr et al.1995,1996; Kriessler&Beers1997;Fadda et al.1998;Kolokotronis et al. 2001;Schuecker et al.2001;Lopes et al.2006).Generally speaking,the sensitivity of substructure detection increases with both increasing statistics(e.g.more galaxies or more X-ray pho-tons)and increasing dimensionality of the test(https://www.doczj.com/doc/ea11610726.html,ing galaxy velocities in addition to their positions,or using X-ray tempera-ture in addition to X-ray surface brightness). Previous investigations have found very di?erent fractions of clusters with substructure in nearby clusters,depending on the method and tracer used for substructure detection,on the cluster sample,and on the size of sampled cluster re-gions(e.g.Geller&Beers1982;Dressler&Shectman1988; Mohr et al.1995;Girardi et al.1997;Kriessler&Beers1997; Jones&Forman1999;Solanes et al.1999;Kolokotronis et al. 2001;Schuecker et al.2001;Flin&Krywult2006;Lopes et al. 2006).Although the distribution of subcluster masses has not been determined observationally,it is known that subclusters of～10%the cluster mass are typical,while more massive subclusters are less frequent(Escalera et al.1994;Girardi et al. 1997;Jones&Forman1999).The situation is probably dif-ferent for distant clusters which tend to show massive sub-structures more often than nearby clusters clearly suggesting hierarchical growth of clusters was more intense in the past (e.g.Gioia et al.1999;van Dokkum et al.2000;Haines et al. 2001;Maughan et al.2003;Huo et al.2004;Rosati et al.2004; Demarco et al.2005;Jeltema et al.2005). Additional evidence for the hierarchical formation of clus-ters is provided by the analysis of brightest cluster galaxies (BCGs hereafter)in substructured clusters.BCGs usually sit evidence of substructure(e.g.Beers et al.1991;Ferrari et al. 2006).From the correlation between cluster and BCG luminosi-ties,Lin&Mohr(2004)conclude that BCGs grow by merg-ing as their host clusters grow hierarchically.The related evo-lution of BCGs and their host clusters is also suggested by the alignement of the main cluster and BCG axes(e.g.Binggeli 1982;Durret et al.1998).Both the BCG and the cluster axes are aligned with the surrounding large scale structure dis-tribution,where infalling groups come from.These infalling groups are?nally identi?ed as substructures once they enter the cluster environment(Durret et al.1998;Arnaud et al.2000; West&Blakeslee2000;Ferrari et al.2003;Plionis et al.2003; Adami et al.2005).Hence,substructure studies really provide direct evidence for the hierarchical formation of clusters. Concerning the impact of subclustering on global cluster properties,it has been found that subclustering leads to over-estimating cluster velocity dispersions and virial masses(e.g. Perea et al.1990;Bird1995;Maurogordato et al.2000),but not in the general case of small substructures(Escalera et al.1994; Girardi et al.1997;Xu et al.2000).During the collision of a subcluster with the main cluster,both the X-ray emitting gas distribution and its temperature have been found to be signi?-cantly a?ected(e.g.Markevitch&Vikhlinin2001;Clowe et al. 2006).As a consequence,it has been argued that substruc-ture can explain at least part of the scatter in the scaling rela-tions of optical-to-X-ray cluster properties(e.g.Fitchett1988; Girardi et al.1996;Barrena et al.2002;Lopes et al.2006). As far as the internal properties of cluster galaxies are concerned,there is observational evidence that a higher frac-tion of cluster galaxies with spectral features characteristic of recent or ongoing starburst episodes is located in sub-structures or in the regions of cluster-subcluster interactions (Caldwell et al.1993;Abraham et al.1996;Biviano et al.1997; Caldwell&Rose1997;Bardelli et al.1998b;Moss&Whittle 2000;Miller et al.2004;Poggianti et al.2004;Miller2005; Giacintucci et al.2006). In this paper we search for and characterize substructures in the sample of77nearby clusters of the WIde-?eld Nearby Galaxy-cluster Survey(WINGS hereafter,Fasano et al.2006). This sample is an almost complete sample in X-ray?ux in the redshift range0.04 2.The Data WINGS is an all-sky,photometric(multi-band)and spectro-scopic survey,whose global goal is the systematic study of the local cosmic variance of the cluster population and of the prop-erties of cluster galaxies as a function of cluster properties and local environment. The WINGS sample consists of77clusters selected from M.Ramella et al.:Substructures in the WINGS clusters3 of the project consists of wide-?eld optical imaging of the se- lected clusters in the B and V bands.The imaging data were col- lected using the WFC@INT(La Palma)and the WFI@MPG(La Silla)in the northern and southern hemispheres,respectively. The observation strategy of the survey favors the uniformity of photometric depth inside the di?erent CCDs,rather than com- plete coverage of the?elds that would require dithering.Thus, the gaps in the WINGS optical imaging correspond to the phys- ical gaps between the di?erent CCDs of the mosaics. During the data reduction process,we give particular care to sky subtraction(also in presence of crowded?elds including big halo galaxies and/or very bright stars),image cleaning(spikes and bad pixels)and star/galaxy classi?cation(obtained with both automatic and interactive tools). According to Fasano et al.(2006)and Varela et al.(2007), the overall quality of the data reported in the WINGS photomet-ric catalogs can be summarized as follows:(i)the astrometric errors for extended objects have r.m.s.～0.2arcsec;(ii)the av-erage limiting magnitude is～24.0,ranging from23.0to25.0; (iii)the completeness of the catalogs is achieved(on average)up to V～22.0;(iv)the total(systematic plus random)photometric r.m.s.errors,derived from both internal and external compar-isons,vary from～0.02mag,for bright objects,up to～0.2mag, for objects close to the detection limit. 3.The DEDICA Procedure We base our search for substructures in WINGS clusters on the DEDICA procedure(Pisani1993,1996).This procedure has the following advantages: 1.DEDICA gives a total description of the clustering pattern, in particular the membership probability and signi?cance of structures besides geometrical properties; 2.DEDICA is scale invariant; 3.DEDICA does not assume any property of the clusters,i.e. it is completely non-parametric.In particular it does not re-quire particularly rich samples to run e?ectively. The basic nature and properties of DEDICA are described in Pisani(1993,1996,and references therein).Here we summarize the main structure of the algorithm and how we apply it to our data sample.The core structure of DEDICA is based on the as-sumption that a structure(or a“cluster”in the algorithm jargon) corresponds to a local maximum in the density of galaxies. We proceed as follows.First we need to estimate the proba-bility density functionΨ(r i)(with i=1,...N)associated with the set of N galaxies with coordinates r i.Second,we need to ?nd the local maxima in our estimate ofΨ(r i)in order to iden-tify clusters and also to evaluate their signi?cance relatively to the noise.Third and?nally,we need to estimate the probability that a galaxy is a member of the identi?ed clusters. 3.1.The probability density DEDICA is a non-parametric method in the sense that it does not require any assumption on the probability density function that it is aimed to estimate.The only assumptions are thatΨ(r i) must be continuous and at least twice di?erentiable. The function f(r i)is an estimate ofΨ(r i)and it is built by using an adaptive kernel method given by: f ka(r m) (3) where a is a scale factor set according to optimal convergence requirements.The limit R of the sequence r m de?ned in Eq.3 depends on the starting position r m=1. lim m→+∞ r m=R(r m=1)(4) We run the sequence in Eq.3at each data position r i.We label each data point with the limit R i=R(r m=1=r i).These limits R i are the position of the peak to which the i?th galaxy belong. In the case that all the galaxies are members of a unique cluster, all the labels R i are the same.At the other extreme each galaxy is a one-member cluster and all R i have di?erent values.All the members of a given cluster belong to the same peak in f ka(r) and have the same R i.We identify cluster members by listing galaxies having the same values of R.We end up withνdi?erent clusters each with nμ(μ=1,...,ν)members. In order to maintain a coherent notation,we identify with the labelμ=0the n0isolated galaxies considered a system of background galaxies.We have:n0=N? νμ=1nμ. 3.3.Cluster Signi?cance and of Membership Probability The statistical signi?cance Sμ(μ=1,...,ν)of each cluster is based on the assumption that the presence of theμ?th cluster causes an increase in the local probability density as well as in the sample likelihood L N=Πi[f ka(r i)]relatively to the value Lμthat one would have if the members of theμ?th cluster were all isolated,i.e.belonging to the background. A large value in the ratio L N/Lμcharacterizes the most im-portant clusters.According to Materne(1979)it is possible to estimate the signi?cance of each cluster by using the likelihood ratio test.In other words2ln(L N/Lμ)is distributed as aχ2vari-able withν?1degrees of freedom.Therefore,once we compute the value ofχ2for each cluster(χ2 S ),we can also compute the signi?cance Sμof the cluster. Here we assume that the contribution to the global density 4M.Ramella et al.:Substructures in the WINGS clusters the identi?ed clusters.This criterion also allows us to estimate the probability that a galaxy is isolated. At the end of the DEDICA procedure we are left with a) a catalog of galaxies each with information on position,mem- bership,local density and size of the Gaussian kernel,b)a cat- alog of structures with information on position,richness,the χ2 S parameter,and peak density.For each cluster we also com- pute from the coordinate variance matrix the cluster major axis, ellipticity and position angle. 4.Tweaking the Algorithm with Simulations In this section we describe our analysis of the performance of DEDICA and the guidelines we obtain for the interpretation of the clustering analysis of our observations. We build simulated?elds containing a cluster with and with-out subclusters.The simulated?elds have the same geometry of the WFC?eld and are populated with the typical number of objects we will analyze.For simplicity we consider only WFC ?elds.Because DEDICA is scale-free,a di?erent sampling of the same?eld of view has no consequence on our analysis. In the next section we limit our analysis to M V,lim≤?16. At the median redshift of the WINGS cluster,z?0.05,this absolute magnitude limit corresponds to an apparent magnitude V lim?21.Within this magnitude limit the representative number of galaxies in our frames is N tot=900. We then consider N tot=N mem+N bkg,with N mem the number of cluster members and N bkg the number of?eld–or background –galaxies.We set N bkg=670,close to the average number of background galaxies we expect in our frame based on typical observed?elds counts,e.g.Berta et al.(2006)or Arnouts et al. (1997).With this choice,we have N mem=230. We distribute uniformly at random N bkg objects.We dis-tribute at random the remaining N mem=230objects in one or more overdensities depending on the test we perform.We popu-late overdensities according to a King pro?le(King1962)with a core radius R core=90kpc,representative of our clusters.We then scale R core with the number of members of the substructure, N S.We use R core=250 N C+N S where N C is the number of objects in the cluster with N mem= N S+N C.This scaling of R core with cluster richness is from Adami et al.(1998a)assuming direct proportionality between cluster richness and luminosity(e.g.Popesso et al.2006). As far as the relative richnesses of the cluster and subcluster are concerned,we consider the following richness ratios r cs= N C/N S=1,2,4,8.With these richness ratios,the number of objects in the cluster are N C=115,153,184,204,and those in subclusters are N S=115,77,46,26respectively. In a?rst set of simulated?elds we place the substructure at 2731pixels(15arcmin)from the main cluster so that they do not overlap.In a second set of simulations,we place main cluster and substructure at shorter distances,683and1366pixels,in order to investigate the ability of DEDICA to resolve structures.At each of these shorter distances we build simulations with both r cs=1 and2. For each richness ratio and/or distance between cluster and subcluster we produce16simulations with di?erent realizations 51015 0 0.2 0.4 0.6 0.8 1 simulation Fig.1.Fraction of recovered members of each substructure for di?erent r cs.The solid line connects substructures with r cs=2 and4 We?ll this frame with a grid of data points at the same density as the average density of the?eld. The?rst result we obtain from the runs of DEDICA on the simulations with varying richness ratio is the positive rate at which we detect real structures.We?nd that we always recover both cluster and substructure even when the substructure only contains N S=1/8N C objects,i.e.26objects(on top of the uni-form background).In other words,if there is a real structure DEDICA?nds it. We also check how many original members the procedure assigns to structures it recovers.The results are summarized in Fig.1.In the diagram,the fraction of recovered members of each substructure is represented by the values of its r cs.The solid line connects substructures with r cs=2and4. From Fig.1it is clear that our procedure recovers a large fraction of members,almost irrespective of the richness of the original structure.It is also interesting to note that the?uctu-ations identi?ed as substructures are located very close to the center of the corresponding simulated substructures.In almost all cases the distance between original and detected substructure is signi?cantly shorter than the mean inter-particle distance. The second important result we obtain from the simulations is the false positive rate,i.e.the fraction of noise?uctuations that are as signi?cant as the?uctuations corresponding to real structures. First of all we need to de?ne an operative measure of the reliability of the detected structures.In fact DEDICA provides a default value Sμ(μ=1,...,ν)of the signi?cance(see Sect.3.3). However,Sμhas a relatively small dynamical range,in particular for highly signi?cant clusters. Density or richness both allow a reasonable”ranking”of structures.However,both large low-density noise?uctuations (often built up from more than one noise?uctuation)and very M.Ramella et al.:Substructures in the WINGS clusters 5 20 40 60 80 0.05 0.1 0.15 0.2 Fig.2.χ2S of simulated noise ?uctuations (solid line).Labels are the r cs of simulated structures at the abscissa corresponding to their χ2S and at arbitrary ordinates. We therefore prefer to use the parameter χ2S which stands at the base of the estimate of S μand which is naturally provided by DEDICA.The main characteristic of χ2S is that it depends both on the density of a cluster relative to the background and on its https://www.doczj.com/doc/ea11610726.html,ing χ2S we classify correctly signi?cantly more structures than with either density or richness alone.In Fig.2we plot the distribution of χ2S of noise ?uctuations (solid line).In the same plot we also mark the r cs of real struc-tures as detected by our procedure.We use labels indicating r cs and place them at the abscissa corresponding to their χ2S and at arbitrary ordinates. Fig.2shows that the structures detected with r cs =1,2are al-ways distinguishable from noise ?uctuations.Substructures with r cs =4or higher,although correctly detected,have χ2S values that are close to or lower than the level of noise. With the second set of simulations,we test the minimum dis-tance at which cluster and subcluster can still be identi?ed as separate entities.We place cluster and substructure (r cs =1,2)at distances d cs =683and 1366pixel.These distances are 1/4and 1/2respectively of the distance between cluster and substructure in the ?rst set of simulations.Again we produce 16simulations for each of the 4cases. We ?nd that at d cs =1366pixel cluster and substructure are always correctly identi?ed.At the shorter distance d cs =683pixel,DEDICA merges cluster and substructure in 1out of 16cases for r cs =1and in 8out of 16cases for r cs =2.With our density pro?le,d cs =683pixel corresponds to d cs ?R c +R s with R c ,R s the radii of the main cluster and of the subcluster respectively. In order to verify the results we obtain for 900data points we produce more simulations with N tot =450,600and 1200.In 0.5 1 1.5 2 2.5 0.5 1 1.5 2 Fig.3.Small symbols correspond to χ2S as a function of the num-ber of members of noise ?uctuations.Crosses,circles,dots and triangles are χ2S for the noise ?uctuations of the simulations with N tot =450,600,900,and https://www.doczj.com/doc/ea11610726.html,rge symbols are χ2S of simulated clusters and subclusters with r cs =1.Horizontal lines mark the levels of χ2S ,threshold .These simulations con?rm the results we obtain in the case N tot =900,and allow us to set a detection threshold,χ2S ,threshold (N tot ),for signi?cant ?uctuations in the analysis of real clusters. We summarize the behavior of the noise ?uctuations in our simulations in Fig.3.In this ?gure,the small symbols corre-spond to χ2S as a function of the number of members of noise ?uctuations.In particular,crosses,circles,dots and triangles are χ2S for the noise ?uctuations of the simulations with N tot =450,600,900,and 1200respectively. The larger symbols are the χ2S of the ?uctuations correspond-ing to simulated clusters and subclusters of equal richness (r cs =1). The 4horizontal lines mark the level of χ2S ,threshold ,i.e.the average χ2 S of the 3most signi?cant noise ?uctuations in each of the 4groups of simulations with N tot =450,600,900,and 1200. The expected increase of χ2S ,threshold with N tot is evident. We note that the only signi?cant di ?erence with these ?nd-ings we obtain from the simulations with r cs =2is that χ2S of simulated clusters and subclusters is closer to χ2 S ,threshold (but still higher). We ?t χ2S ,threshold with N tot and obtain log(χ2S ,threshold )=1/2.55log(N tot )+0.394 (5) in good agreement with the expected behavior of the poissonian ?uctuations. As a ?nal test we verify that infra-chip gaps do not have a 6M.Ramella et al.:Substructures in the WINGS clusters chip gap cuts through the center of the structures,DEDICA is able to identify these structures correctly. We summarize here the main results of our tests on simulated clusters with substructures: –DEDICA successfully detects even the poorest structures above a uniform poissonian noise background. –DEDICA recovers a large fraction(typically>3/4)of the real members of a substructure,almost irrespective of the richness of the structure. –DEDICA is able to distinguish between noise?uctuations and true structures only if these structures are rich enough. In the case of our simulations,structures have to be richer than1/4th of the main structure. –DEDICA is able to separate neighboring structures provided they do not overlap. –infra-chip gaps do not threaten the detection of structures that are rich enough to be reliably detected. –theχ2 S threshold we use to identify signi?cant structures is a function of the total number of points and can be scaled within the whole range of numbers of galaxies observed within our?elds. In the next section we apply these results to the real WINGS clusters. 5.Substructure detection in WINGS clusters We apply our clustering procedure to the77clusters of the WINGS sample.The photometric catalog of each cluster is deep, reaching a completeness magnitude V complete≤22.The num-ber of galaxies is correspondingly large,from N gal?3,000to N gal?10,000. The large number of bright background galaxies(faint ap-parent magnitudes)dilutes the clustering signal of local WINGS clusters.We perform test runs of the procedure on several clus-ters with magnitude cuts brighter than V complete.Based on these tests,we decide to cut galaxy catalogs to the absolute magnitude threshold M V=?16.0.With this choice a)we maximize the signal-to-noise ratio of the detected subclusters and b)we still have enough galaxies for a stable identi?cation of the system.At the median redshift of WINGS clusters,z?0.0535,our absolute magnitude cut corresponds to an apparent magnitude V?21.2. This apparent magnitude also approximately corresponds to the magnitude where the contrast of our typical cluster relative to the?eld is maximum (this estimate is based on the average cluster luminosity function of(Yagi et al.2002;De Propris et al. 2003)and on the galaxy counts of(Berta et al.2006)). The number of galaxies that are brighter than the threshold M V=?16.0is in the range600 In order to proceed with the identi?cation of signi?cant structures within WINGS clusters,we need to verify that our simulations are su?ciently representative of the real cases. In practice we need to compare the observed distributions of χ2 S values of noise?uctuations with the corresponding simulated distributions.In the observations it is impossible to identify indi-vidual?uctuations as noise.In order to have an idea of the distri- butions ofχ2 S of noise?uctuations we consider that our?elds are 0204060 0.05 0.1 0.15 0.2 0.25 Fig.4.χ2 S distributions for border(thick solid histogram)and central(thick dashed histogram)observed?uctuations.The thin solid line is the normalized distribution ofχ2 S of the noise?uctu- ations in our simulations We therefore consider separately the?uctuations within the central regions of the frames and all other?uctuations(borders). We de?ne the central regions as the central10%of WFC and WFI areas.We plot in Fig.4the two distributions.The thick solid histogram is for the border and the thick dashed histogram for the center of the frames.The di?erence between”noise”and ”signal”is clear.In the same?gure we also plot the normal- ized distribution ofχ2 S of the noise?uctuations in our simulations (thin solid line).The distributions ofχ2 S of the observed and sim- ulated?uctuations are in reasonable agreement considering a) the simple model used for the simulations and that b)in the ob- servations we can not exclude real low-χ2 S structures among noise ?uctuations.We conclude that for our clusters we can adopt the same reliability thresholdχ2 S,threshold we determine from our sim- ulations(Eq.5). 6.The Catalog of Substructures We detect at least one signi?cant structure in55(71%)clus- ters.We?nd that12clusters(16%)have no structure above the threshold(undetected).In the case of another10(13%)clusters we?nd signi?cant structures only at the border of the?eld of view.In absence of a detection in the center of the frame,we con- sider these border structures unrelated to the target cluster.We also verify that in the Color-Magnitude Diagram(CMD)these border structures are redder than expected given the redshift of the target cluster.We consider also these10clusters undetected. Here we list the22undetected clusters:A0133,A0548b, A0780,A1644,A1668,A1983,A2271,A2382,A2589,A2626, A2717,A3164,A3395,A3490,A3497,A3528a,A3556,A3560, A3809,A4059,RX1022,Z1261. M.Ramella et al.:Substructures in the WINGS clusters 7 Fig.5.Isodensity contours (logarithmically spaced)of the Abell 85?eld.The title lists the coordinates of the center.The orien-tation is East to the left,North to the top.Galaxies belonging to the systems detected by DEDICA are shown as dots of di ?erent colors.Black,light green,blue,red,magenta,dark green are for the main system and the subsequent substructures ordered as in Table https://www.doczj.com/doc/ea11610726.html,rge symbols are for galaxies with M V ≤?17.0that lie where local densities are higher than the median local density of the structure the galaxy belongs to.Open symbols mark the positions of the ?rst-and second-ranked cluster galaxies,BCG1and BCG2respectively.Similar plots for the 55analysed clusters are available in the electronic version of this Journal. result of the division into too many structures of the total avail-able clustering signal in the ?eld (or of a too large fraction of the clustering signal going into border structures).Several phys-ical situations could be at the origin of missed detections.One possibility is an excess of physical substructures of comparable richness.Another possibility is that these clusters are embedded in regions of the large scale structure that are highly clustered.We do not try to recover these structures because they can not be prominent enough.Since our analysis is bidimensional,we can only detect and use con?dently the most prominent struc-tures.Redshifts are needed for a more detailed analysis of cluster substructures. We list the 55clusters with signi?cant structures in Table A.1.We give,for each substructure:(1)the name of the parent cluster;(2)the classi?cation of the structure as main (M),sub-cluster (S),or background (B)together with their order number;(3)right ascension (J2000),and (4)declination (J2000)in deci-mal degrees of the DEDICA peak;the parameters of the ellipse we obtain from the variance matrix of the coordinates of galaxies in the substructure,i.e.(5)major axis in arcminutes,(6)elliptic-ity,and (7)position angle in degrees;(8)luminosity (see the next section);(9)χ2S . We make available contour plots of the number density ?elds systems detected by DEDICA are shown as dots of di ?erent col-ors.We use large symbols for brighter galaxies (M V ≤?17.0)that lie where local densities are higher than the median local density of the structure the galaxy belongs to.We also mark with open symbols the positions of the ?rst-and second-ranked cluster galaxies,BCG1and BCG2respectively.Color coding is black,light green,blue,red,magenta,dark green for the main system and the subsequent substructures ordered as in Table A.1. We describe and analyze in detail our catalog in the next sec-tion. 7.Properties of substructures The ?rst problem we face in order to study the statistical and physical properties of substructures is to determine their asso-ciation with the main structure.In fact,the main structure itself has to be identi?ed among the structures detected by DEDICA in each frame. In most cases it is easy to identify the main structure of a cluster since it is located at the center of the frame and it has a high χ2S .In two cases (A0168and A1736)the choice of the main structure is complicated because there are several similar structures near the center of the frame.In these cases we select the main structure for its highest χ2S . At this point we limit our analysis to members of the struc-ture that a)have an absolute magnitude M V ≤?17(corrected for Galactic absorption)and that b)are in the upper half of the distri-bution of DEDICA-de?ned local galaxy densities of the system they belong to.The galaxy density threshold we apply allows us to separate adjacent structures whose de?nition becomes more uncertain at lower galaxy density levels.The magnitude cut in-creases the relative weight of the galaxies we use to evaluate the nature of structures in the CMD. After having identi?ed the main structure,we need to deter-mine which structures in the ?eld of view of a given cluster have to be considered background structures.We consider a structure a physical substructure (or subcluster)if its color-magnitude re-lation (CMR hereafter)is identical,within the errors,to the CMR of the main structure. As a ?rst step we de?ne the color-magnitude relation (CMR)of the “whole cluster”,i.e.of galaxies in the main structure to-gether with all other galaxies not assigned to any structure by DEDICA.We compute the (B ?V )CMR of the Coma cluster from published data (Adami et al.2006).Then we keep ?xed the slope of the linear CMR of Coma and shift it to the mean redshift of the cluster. In order to determine that the main structure and a substruc-ture are at the same redshift,we evaluate the fraction of back-ground (red)galaxies,f bg ,that each structure has in the CMD.If these fractions are identical within the errors (Gehrels 1986),we consider the two structures to be at the same redshift. In practice we determine f bg by assigning to the background those galaxies of a structure that are redder than a line parallel to the CMR and vertically shifted (i.e.redwards)by 2.33times the root-mean square of the colors of galaxies in the CMR.We note that the probability that a random variable is greater than 2.33in a Gaussian distribution is only 1%. The result of the selection of main structures and substruc-tures is the following:40clusters have a total of 69substruc-tures at the same redshift as the main structure,only 15clus-ters are left without substructures.A total of 35systems are 8M.Ramella et al.:Substructures in the WINGS clusters Fig.7.Cumulative distributions of the two di ?erent indicators of subclustering:left panel N sub ,right panel f Lsub . De Propris et al.2003),c)the total area covered by the 55clus-ter ?elds,and d)the limiting apparent magnitude corresponding to our absolute magnitude threshold M V =?16.0,we expect to ?nd ～0.5±0.2background systems per cluster ?eld,28±11in total.This estimate is consistent with the 35background systems we ?nd. The fraction of clusters with subclusters (73%)is higher than generally found in previous investigations (typically ～30%,see,e.g.,Girardi &Biviano 2002;Flin &Krywult 2006;Lopes et al.2006,and references therein).Even if we count all undetected clusters as clusters without substructures,this fraction only de-creases to 52%(40/77).It is however acknowledged that the fraction of substructured clusters depends,among other factors,on the algorithm used to detect substructures,on the quality and depth of the galaxy catalog.For example Kolokotronis et al.(2001)using optical and X-ray data ?nd that the fraction of clus-ters with substructures is ≥45%,Burgett et al.(2004)using a battery of tests detect substructures in 84%of the 25clusters of their sample. Having established the “global”fraction of substructured clusters,we now investigate the degree of subclustering of in-dividual clusters,i.e.the distribution of the number of substruc-tures N sub we ?nd in our sample. We ?nd 15(27%)clusters without substructures;22(40%)clusters with N sub =1;10(18%)clusters with N sub =2;6(11%)clusters with N sub =3;and 2(3%)clusters with N sub =4.We plot in the left panel of Fig.7the integral distribution of N sub .The distribution of the level of subclustering does not change when we measure it as the fractional luminosity of subclusters,f Lsub ,relative to the luminosity of the whole cluster (see Fig.7,right panel).The luminosities we estimate are background cor-rected using the counts of Berta et al.(2006).We use the ellipses output from DEDICA (see previous section)as a measure of the area of subclusters. We ?nd that N sub and f Lsub are clearly correlated according to the Spearman rank-correlation test. We now consider the distribution of subcluster luminosities and plot the corresponding histogram in Fig.8.In the same ?g-ure we also plot with arbitrary scaling the power-law ∝L ?1.This Fig.8.Observed di ?erential distribution of subcluster lumi-nosities (histogram)and theoretical model (arbitrary scaling;De Lucia et al.2004). Our observations are consistent to within the uncertainties with the theoretical prediction of De Lucia et al.(2004)down to L ～1011.2L ⊙.The disagreement at lower luminosity is ex-pected since:a)below this limit galaxy-sized halos become im-portant among the simulated substructures,and b)only above this limit we expect our catalog to be complete.In fact only subclusters with luminosities brighter than L =1011.2L ⊙have always richnesses that are ≥1/3of the main structure.This richness limit approximately corresponds to the completeness limit of DEDICA detections according to our simulations (see Sect.4). 8.Brightest Cluster Galaxies Here we investigate the relation between BCGs and cluster struc-tures. We ?nd that,on average,BCG1s are located close to the den-sity peak of the main structures.In projection on the sky,the bi-weight average (see Beers et al.1990)distance of BCG1s from the peak of the main system is 72±11kpc.If we only consider the 44BCG1s that are on the CMR and are assigned to main systems by DEDICA,the average distance decreases to 56±8kpc.The fact that BCG1s are close to the center of the system is consistent with current theoretical view on the formation of BCGs (e.g.Dubinski 1998;Nipoti et al.2004). BCG2s are more distant than BCG1s from the peak of the main system:the biweight average distance is 345±47kpc.If we only consider the 26BCG2s that are on the CMR and are assigned to main systems by DEDICA,the average distance de-creases to 161±34kpc. Projected distances of BCG2s from density peaks remain larger than those of BCG1s even when we consider the density peak of the structure or substructure they belong to.In Fig.9we plot the cumulative distributions of the distances of BCG1s (solid line)and BCG2s (dashed line)from the density peak of their systems.The distributions are di ?erent at the >99.99%level according to a Kolmogorov-Smirnov test (KS-test). Now we turn to luminosities and ?nd that the magnitude dif-ference between BCG1s and BCG2s,?M 12,is larger in clus- M.Ramella et al.:Substructures in the WINGS clusters 9 Fig.9.Cumulative distributions of distances of BCG1(solid line)and BCG2(dashed line)from the density peak of their sys- tem. Fig.10.Cumulative distributions of the magnitude di?erence be- tween BCG1and BCG2in clusters with(dashed line)and with- out subclusters(solid line). The two distributions are di?erent according to a KS-test at the 99.1%con?dence level.We note that Lin&Mohr(2004)?nd that?M12is independent of cluster properties.These authors however do not consider subclustering. In order to determine whether the higher values of?M12in clusters without subclusters are due to an increased luminosity of the BCG1(L1)or to a decreased luminosity of the BCG2 (L2),we consider the luminosity of the10th brightest galaxy (L10)as a reference.The biweight average luminosity ratios are ters without substructures,and conclude that the?M12-e?ect is caused by a brightening of the BCG1relative to the BCG2in clusters without substructures. The fact that?M12is higher in clusters without substruc- tures can be interpreted,at least qualitatively,in the framework of the hierarchical scenario of structure evolution.Clusters with- out substructures are likely to be evolved after several merger Some of these galaxies may even have been BCGs of the merg- ing structures.The simulations by De Lucia&Blaizot(2006) show that the BCG1s continue to increase their mass via can- nibalism even at recent times,and that there is a large vari- ance in the mass accretion history of BCG1s from cluster to cluster.The result of such a cannibalism process is an increase of the BCG1luminosity with respect to other cluster galaxies, and in extreme cases may lead to the formation of fossil groups (Khosroshahi et al.2006). However,according to these simulations,only15%of all BCG1s have accreted>30%of their mass over the last2Gyr, while another15%have accreted<3%of their mass over the same period.Our results indicate that about60%of the BCG1s are more than1magnitude brighter than the corresponding BCG2s.Given the size and generality of the luminosity dif- ferences it would seem that cannibalism alone,even if present along the merging history of a given cluster,cannot account for it.Most of the BCG1s should have then been assembled in early times,as pointed out in the downsizing scenario for galaxy for- mation(Cowie et al.1996)and entered that merging history al- ready with luminosity not far form the present one. 9.Summary In this paper we search for and characterize cluster substructures, or subclusters,in the sample of77nearby clusters of the WINGS (Fasano et al.2006).This sample is an almost complete sample in X-ray?ux in the redshift range0.04 We detect substructures in the spatial projected distribution of galaxies in the cluster?elds using DEDICA(Pisani1993, 1996)an adaptive-kernel technique.DEDICA has the following advantages for our study of WINGS clusters: a)DEDICA gives a total description of the clustering pat- tern,in particular membership probability and signi?cance of structures besides geometrical properties. b)DEDICA is scale invariant c)DEDICA does not assume any property of the clusters,i.e. it is completely non-parametric.In particular it does not require particularly rich samples to run e?ectively. In order to test DEDICA and to set guidelines for the in- terpretation of the results of the application of DEDICA to our observations we run DEDICA on several sets of simulated?elds containing a cluster with and without subclusters. We?nd that:a)DEDICA always identi?es both cluster and subcluster even when the substructure richness ratio cluster- to-subcluster is r cs=8,b)DEDICA recovers a large fraction of members,almost irrespective of the richness of the original structure(>～70%in most cases),c)structures with richness ra- tios r cs<～3are always distinguishable from noise?uctuations of the poissonian simulated?eld. These simulations also allow us to de?ne a threshold that we use to identify signi?cant structures in the observed?elds. We apply our clustering procedure to the77clusters of the WINGS sample.We cut galaxy catalogs to the absolute magni- tude threshold M V=?16.0in order to maximize the signal-to- noise ratio of the detected subclusters. We detect at least one signi?cant structure in55(71%)clus- ter?elds.We?nd that12clusters(16%)have no structure above the threshold(undetected).In the remaining10(13%)clusters we?nd signi?cant structures only at the border of the?eld of 10M.Ramella et al.:Substructures in the WINGS clusters than expected given the redshift of the target cluster.We consider also these clusters undetected. We provide the coordinates of all substructures in the55 clusters together with their main properties. Using the CMR of the early-type cluster galaxies we sep-arate”true”subclusters from unrelated background structures. We?nd that40clusters out of55(73%)have a total of69sub-structures with15clusters left without substructures. The fraction of clusters with subclusters(73%)we identify is higher than most previously published values(typically～30%, see,e.g.,Girardi&Biviano2002,and references therein).It is however acknowledged that the fraction of substructured clus-ters depends,among other factors,on the algorithm used to de-tect substructures,on the quality and depth of the galaxy catalog (Kolokotronis et al.2001;Burgett et al.2004). Another important result of our analysis is the distribution of subcluster luminosities.In the luminosity range where our sub-structure detection is complete(L≥1011.2L⊙),we?nd that the distribution of subcluster luminosities is in agreement with the power-law∝L?1predicted for the di?erential mass function of substructures in the cosmological simulations of De Lucia et al. (2004). Finally,we investigate the relation between BCGs and clus-ter structures. We?nd that,on average,BCG1s are located close to the den-sity peak of the main structures.In projection on the sky,the bi-weight average distance of BCG1s from the peak of the main system is72±11kpc.BCG2s are signi?cantly more distant than BCG1s from the peak of the main system(345±47kpc). The fact that BCG1s are close to the center of the system is consistent with current theoretical view on the formation of BCGs(Dubinski1998). A more surprising result is that the magnitude di?erence be-tween BCG1s and BCG2s,?M12,is signi?cantly larger in clus-ters without substructures than in clusters with substructures. This fact may be interpreted in the framework of the hierarchical scenario of structure evolution(e.g.De Lucia&Blaizot2006). 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the parent cluster;(2)the classi?cation of the structure as main(M),subcluster(S),or background(B)together with their order number;(3)right ascension (J2000),and(4)declination(J2000)in decimal degrees of the DEDICA peak;the parameters of the ellipse we obtain from the variance matrix of the coordinates of galaxies in the substructure,i.e.(5)major axis in arcminutes,(6)ellipticity,and(7)position . angle in degrees;(8)luminosity;(9)χ2 S ID classαJ2000δJ2000a e PA Lχ2 S (deg)(deg)(arcmin)(deg)(1012L⊙) A0085M10.4752-9.3025 2.00.23-17.0.4153648.4 A0085S110.4410-9.4430 1.80.35-39.0.1764942.9 A0085S210.3947-9.3501 2.30.40-72.0.1233732.8 A0119M14.0625-1.2630 4.10.44-65.0.8395563.4 A0119S114.1183-1.2106 4.60.60-23.0.2684750.8 A0119S214.0267-1.0441 3.40.3480.0.0359232.0 A0119B113.9402-1.4979 3.10.3946.–23.5 A0147M17.0648 2.2033 3.90.4579.0.3139245.2 A0147S116.8673 2.1393 4.10.25-50.0.0563824.8 A0147S217.1925 1.9284 4.40.3855.0.0505221.4 A0147B117.0753 2.3174 4.40.3775.–58.0 A0151M17.2186-15.4219 1.70.26-16.0.4734439.9 A0151S117.3516-15.3652 2.10.37-58.0.1376142.9 A0151S217.2632-15.5564 1.60.26-53.0.1976240.9 A0151B117.1375-15.6116 1.50.08-4.–59.0 A0160M18.234415.5126 3.60.3782.0.5552566.7 A0160S118.248315.3138 5.00.4185.0.0312038.1 A0160S218.114115.7501 3.00.1686.0.1519628.3 A0160S317.998115.4150 3.90.410.0.0631527.8 A0168M18.77550.3999 3.10.32-11.0.2449230.5 A0168S118.87990.2993 2.00.33 4.0.0687128.6 A0193M21.28948.6994 2.10.0836.0.61982105.7 A0193B120.99458.6119 4.90.45-1.–39.1 A0311M32.379319.7722 2.30.1943.0.4332044.0 A0376M41.427636.9517 1.70.07-67.0.1347740.8 A0376S141.556936.9214 4.40.49-22.0.2435033.6 A0500M69.6476-22.1308 2.00.3116.0.4120345.5 A0500S169.5915-22.2377 2.30.1936.0.2052047.3 A0602M118.363829.3528 1.80.55-46.0.2011255.4 A0602S1118.184829.4145 2.30.5231.0.0847034.8 A0671M127.123730.4269 1.60.24-51.0.6858269.8 A0671S1127.224130.4342 2.00.40-5.0.1973644.3 A0671S2127.161730.2967 1.90.24-90.0.1377843.0 A0754M137.1073-9.6370 2.00.2553.0.5606346.8 A0754S1137.3707-9.6760 3.20.53-8.0.3059054.9 A0754S2137.2619-9.6367 1.70.1476.0.2373451.2 A0957x M153.4095-0.9259 2.00.09-83.0.4210638.6 A0957x B1153.5517-0.7023 2.20.44-63.–37.9 A0970M154.3595-10.6921 1.50.27-30.0.4613062.5 A0970S1154.2369-10.6422 1.70.1515.0.1366042.3 A0970B1154.1833-10.6771 1.80.23-76.–32.2 A1069M159.9418-8.6883 2.80.3152.0.3727050.2 A1069S1159.9286-8.5506 2.40.2388.0.1853232.7 A1069B1159.7678-8.9262 3.50.5577.–54.7 A1291M173.046756.0255 2.50.51-11.0.2527232.1 A1291S1172.909056.1872 1.40.48-82.0.0353037.6 A1631a M193.2410-15.3413 1.40.3540.0.2007733.9 A1736M202.0097-27.3131 3.10.3558.0.4182452.1 A1736S1201.7305-27.0170 2.80.329.0.2402342.6 A1736S2201.7662-27.4067 3.40.287.0.1452842.3 A1736S3201.5672-27.4291 2.70.4473.0.1692640.4 M.Ramella et al.:Substructures in the WINGS clusters13 ID classαJ2000δJ2000a e PA Lχ2 S (deg)(deg)(arcmin)(deg)(1012L⊙) A1795S1207.232926.7362 1.30.3882.0.0512346.7 A1831M209.812027.9714 1.90.439. 1.0841856.0 A1831S1209.735628.0636 2.10.3441.0.3629559.7 A1831B1209.572528.0206 1.70.25-10.–47.7 A1991M223.640518.6390 2.30.54-78.0.2819540.3 A1991S1223.757518.7812 2.50.3241.0.1141249.9 A1991B1223.768318.7022 1.70.3171.–36.6 A2107M234.949721.8075 2.70.1948.0.5099461.1 A2107B1235.069922.0127 4.30.4883.–32.9 A2107B2235.140921.8276 2.40.1055.–20.4 A2124M236.240036.0990 1.30.2432.0.4172743.3 A2124B1236.020736.1779 1.60.2234.–59.7 A2149M240.372353.9406 1.50.46-10.0.3734748.7 A2169M243.486749.18750.60.2272.0.1535834.2 A2256M255.926078.6412 1.90.29-86. 1.4656395.6 A2256B1256.309478.4886 2.20.4875.–48.2 A2256B2256.602478.4283 2.00.12-88.–46.8 A2399M329.3693-7.7772 3.50.64-26.0.4050538.8 A2415M331.3829-5.5444 2.30.23-60.0.3678044.8 A2415S1331.5610-5.3960 2.30.3352.0.0503233.9 A2415B1331.3800-5.4017 1.90.3432.–41.4 A2415B2331.3295-5.3890 1.60.41 4.–37.3 A2457M338.9462 1.4765 4.30.50-84.0.88720107.3 A2457S1339.0392 1.6459 4.10.65-50.0.0596023.1 A2457B1339.0667 1.3266 5.60.5377.–73.8 A2572a M349.319218.7197 2.90.3923.0.4474947.8 A2572a S1349.112218.5320 4.10.258.0.0732034.5 A2572a S2349.385118.5395 2.60.3167.0.0534525.1 A2572a S3349.003718.7220 3.20.3486.0.0088420.5 A2593M351.076614.6539 1.10.2558.0.2833333.8 A2593S1351.067714.4048 2.20.4280.0.0981027.0 A2622M353.738427.3856 3.10.0976.0.4892068.1 A2622S1353.488027.2877 4.20.3546.0.0307035.1 A2622B1353.783727.3182 3.00.49-5.–53.7 A2622B2353.800927.6217 2.60.3868.–29.9 A2657M356.17259.1818 4.50.4722.0.2706149.6 A2657S1356.27559.1799 3.20.3510.0.2077134.0 A2657B1355.95698.9422 2.80.06-10.–38.6 A2665M357.7050 6.1582 3.60.2671.0.67950121.8 A2665S1357.4003 5.8659 4.90.6484.0.0178017.5 A2665B1357.8218 6.3522 3.50.44-50.–32.7 A2734M 2.8363-28.8652 3.40.33 3.0.4870056.3 A2734S1 2.6950-28.7728 4.00.27-7.0.1897055.0 A2734S2 2.6987-29.0394 3.20.2455.0.0303043.3 A2734S3 2.5727-29.0562 3.40.5184.0.0310033.1 A2734B1 2.7701-28.6488 3.70.3914.–28.0 A3128M52.4825-52.5764 2.20.17-36.0.4045237.2 A3128S152.7366-52.7089 4.10.44-9.0.2524051.8 A3128S252.6655-52.4413 2.30.32-65.0.1964651.0 A3128S352.3697-52.7570 3.20.4781.0.1716939.0 A3158M55.7477-53.6334 2.60.62 2.0.7020552.8 A3158S155.8382-53.6780 3.40.5810.0.4555353.4 A3266M67.7893-61.4637 1.10.27-72.0.4299363.5 A3376M90.1628-39.9950 2.50.14-43.0.3370843.1 A3376S190.4344-39.9776 2.70.20-4.0.2127959.8 A3376S290.4712-39.7946 2.10.39-88.0.0090431.2 A3528b M193.5928-29.0136 1.30.04-24.0.6563866.4 A3528b S1193.6030-29.0721 1.30.2610.0.1670659.0 A3530M193.9098-30.3606 1.90.2633.0.5304334.9 14M.Ramella et al.:Substructures in the WINGS clusters ID classαJ2000δJ2000a e PA Lχ2 S (deg)(deg)(arcmin)(deg)(1012L⊙) A3558M201.9587-31.4892 4.90.5449. 1.1486064.1 A3558B1202.2501-31.6887 2.60.4944.–37.6 A3562M203.4603-31.6812 2.50.1882.0.3908742.4 A3562S1203.1622-31.7742 4.00.40-86.0.1501051.1 A3562S2203.3137-31.6953 3.60.4076.0.1170641.0 A3562S3203.6982-31.7171 2.50.21-50.0.0782036.7 A3562S4203.6541-31.5969 4.00.55-81.0.0654230.3 A3667M303.1637-56.8598 2.10.1423.0.5680342.0 A3667S1303.5297-56.9660 3.00.39-86.0.2708639.2 A3667S2302.7241-56.6674 1.50.1775.0.2894834.0 A3667S3302.7081-56.7557 2.30.3912.0.0996133.7 A3716M312.9910-52.7677 4.70.3836.0.7645077.9 A3716S1312.9769-52.6434 3.50.22-6.0.4915956.9 A3716B1312.7735-52.8976 4.10.4028.–48.4 A3716B2313.1888-52.4785 3.20.2321.–22.6 A3880M336.9796-30.5474 3.80.33-50.0.2584044.1 A3880B1336.8684-30.8171 2.50.3064.–30.9 A3880B2336.7356-30.7839 2.70.3546.–28.4 IIZW108M318.4443 2.5706 2.60.3842.0.4994033.4 IIZW108S1318.6247 2.5533 3.20.36-14.0.0856542.3 IIZW108B1318.3335 2.7751 1.60.2443.–44.5 IIZW108B2318.5190 2.8039 2.50.4422.–33.6 MKW3s M230.39167.7281 2.30.30-8.0.3761448.7 MKW3s S1230.45767.8769 3.30.46-22.0.0458539.3 MKW3s B1230.73497.8882 2.00.4016.–25.5 RX0058M14.587526.8816 2.40.22-31.0.3196744.2 RX0058S114.765227.0424 3.80.6064.0.3166150.6 RX0058B114.401226.7041 3.30.08-12.–28.9 RX1740M265.139835.6416 2.80.21-26.0.1789642.1 RX1740S1265.260035.4366 3.30.2238.0.0194631.7 RX1740S2264.868835.6053 3.70.5228.0.0134027.9 RX1740S3265.074435.8116 3.50.44-7.0.0116621.8 Z2844M150.728132.6483 2.90.20-85.0.1014348.3 Z2844S1150.652432.7621 5.20.5863.0.0493050.5 Z2844S2150.582132.8890 2.60.390.0.0039523.1 Z8338M272.744749.9078 3.00.37-67.0.4587643.1 Z8338S1272.860649.7916 3.20.1162.0.0554931.9 Z8338S2272.690349.9737 3.10.6762.0.0708925.7 Z8338B1272.447949.6815 1.70.189.–25.8 Z8852M347.60247.5824 2.70.41-45.0.7611067.9 Z8852S1347.59267.3999 5.80.5662.0.1202232.4 Z8852S2347.69867.8018 2.30.1381.0.0249325.5 Z8852B1347.73817.6808 2.10.25-73.–35.9 Z8852B2347.49517.8165 2.40.2172.–27.0 Online Material 韩国十大当红女子组合 NO.10：kara K ARA是韩国DSP公司2007年重点打造的新人女子组合，而曾经成功打造了红极一时的女子组合Fin.K.L的DSP也有意再创一个女子组合的高峰时代。KARA这个名字是取自于希腊语"CHARA",有着喜悦的意思，也希望通过KARA 美妙的声音把喜悦带给大家。成员是由5位的可爱女生组成分别是：队长朴奎利、主唱韩胜妍、Rapper郑妮可、领舞具荷拉以及老小姜智英（知英）虽然成员的年纪都比较小却已在DSP练习多年，实力不俗。2010年8月进军日本取得不俗的成绩。 ：secret S ecret组合是韩国娱乐公司TS Entertainment 继嘻哈组合Untouchedable之后推出的四人女子组合，由全孝盛，郑荷娜，宋智恩和韩善花组成。出道前以出道实录节目《Secret Story》为众人熟悉，随着首只出道单曲《I Want You Back》2009年10月13日正式出道，之后还有《Magic》、《Madonna》等知名神曲面世，中毒性旋律让人朗朗上口. NO.8：A pink A Pink（韩文：????）是韩国6人新人女子音乐团体，为韩国Cube Entertainment的子公司A Cube Entertainment第一个组合。她们的粉丝被称为Pink Panda，团名A Pink是从网上征选到的，意为开始时任何东西都没有的纯白画布慢慢的我们会亲手绘上我们的愿望，和现时韩国女乐团主打性感风不同的是A Pink清纯的出道形象而引来人们注目，歌曲风格清新自然，简单俏皮！ ：Rainbow R ainbow是由推出过水晶男孩，FINKL，SS501，KARA等人气组合的DSP公司花了4年的时间培养出来的女子组合。因为有7名成员，所以她们在练习生时期就被称为Rainbow。 （1）JYP 以下转载自新浪娱乐 JYP简介： https://www.doczj.com/doc/ea11610726.html,/ JYP的全称和上面一样都是JYP Entertainment,名字是老板朴振英的英文缩写,只是P就是朴去了后面,说起朴振英可能看过XMAN和情书的人都知道很多人会用他的歌去跳舞,他在当年可是号称韩国的杰克逊的....现在红遍全亚洲的RAIN 当时还只是他的伴舞而已,RAIN后来也当过朴志胤的伴舞, 八年前，以热情的舞蹈和深厚的演唱势力在MV中出现的朴振荣还仅仅是一名歌手。数年后的今天，还是他，从Dancer到歌手，从歌手到作曲家，从作曲家到总策划，不知不觉已经成为“朴振荣社团”的领导人，引导他旗下的成员在歌坛占据一片天地。在他的严格训练下，GOD，RAIN，星，Noel，林贞熙以及之前因《我是男人》、《成人礼》而走红的朴志润，早已成为韩国乐坛的中坚歌手或是最值得期待的新人。而由朴振荣一手创建的JYP Entertainment依然成为韩国最大的“梦工厂”之一。 从小酷爱舞蹈的朴振荣，拥有延世大学地质学和政治学的硕士学位。他以歌手出道初期，业内人士以“自由自在的音乐追求”来比喻这位音乐天才，其做音乐时的自由奔放在歌迷之间引起了共鸣。但是梦想成为音乐制作人的他在略有名气之后，开始策划建造属于自己的“梦工厂”。1997年11月，朴振荣在家族成员， 朋友的帮助下以5000万韩元注册了一家名为“泰宏”的小型经纪公司，这就是如今JYP的前身，当然旗下歌手也只有朴振荣自己。公司成立之初，朴振荣力志将全部心力贡献于韩国音乐的开发与制作事业，以音乐的先进化和世界化为目标，力求充分结合韩国海内外的音乐优势，拓展韩国的流行音乐，为韩国的民族文化步入国际领域竭尽所能。2001年4月，泰宏公司正式更名为JYP Entertainment(简称JYP)，并适应潮流将之前的家族经营模式改为股份制企业，同时成功融资35亿韩元。 完美的品质，一流的音乐制作水准，有赖于经验丰富的专业人员。JYP成功改革后，分为唱片企划部，海外事业部，产业联系部，三大部门。其中，唱片企划部，负责新人发掘以及现有歌手的唱片制作。该部分又分为作业室，练习室，录音室，以及公司艺人宿舍等多个单独的活动部门。该部分作为JYP培养歌手的摇篮，多年来成绩斐然，备受韩国海内外众多音乐人士的关注。该部分通过仔细钻研唱片市场，歌手的特点等制作出众多大卖唱片，为韩国的流行音乐进入主流音乐市场提供了一定的发展空间。其制作的唱片，在艺术欣赏和商业两个层面均取得了较大成功，并在中韩两国形成强烈的JYP风格。海外事业部，专门负责公司签约歌手的海外唱片宣传。而产业联系部，专门负责开发音乐，音乐录影带，广告，影视作品等多种衍生产业的衔接。此外，除了制作出色的音乐，JYP 在平面设计领域也赢得了极高的荣誉。2002年5月，JYP正式成立平面设计室，该部门全面包揽了JYP所出版的音像制品的唱片封套，宣传品，JYP网站以及明星广告的设计工作，更进一步为乐界，广告界以及公司的企业宣传服务。 韩国TAKE组合成员 中文- 李承铉(译：Lee Seung Hyun) 韩文名- ??? 英文名- Nathan Lee 出生日期- 1984年10月30日 星座- 天蝎座 身高体重- 179cm, 64kg, 280mm 鞋码- 43码~44码（欧码） 出道时间：2003年3月28日 毕业学校–檀国大学影视演艺科 宗教- 基督教 绰号– Beckham（贝克汉姆） 家庭成员- 父母和妹妹(Joanne) 血型- O型 爱好–看电影, 听音乐, 购物 特长– Rap 习惯- 无 减压法–听音乐或者拿起身边的东西就扔 最有魅力的部位–小嘴 1号宝物–首饰和礼物 喜欢的音乐–除了古典的都喜欢 喜欢的音乐人– Joaan, Nelly 练歌房18号- X-japan的Endless rain 喜欢的饮食–鸡肉和饼干, 糖等零食 喜欢的颜色–黑色, 白色 喜欢的季节–春天 喜欢的服装风格- 休闲装 喜欢的运动–高尔夫 性格(优缺点)- 活泼但是急噪 理想型–心地善良地女孩 座右铭–不求最高，但求竭尽全力 音乐观–世间不能没有音乐，音乐给人快乐 对于TAKE组合发展的期望值是- 有实力，什么都很棒的组合 圈内好友： 胡定欣，谢娜，君君，柳岩，王恩琦，俞灏明，张峻宁 电视剧作品 不详韩国KBS电视台电视剧《卢家的罗曼斯》 2001 韩国SBS电视连续剧《守护天使》 2007 中国电视连续剧《会有天使替我爱你》（扮演男主人公尹堂曜）剧情介绍： 姓名：尹堂曜 出处：《会有天使替我爱你》 学校：圣榆商学院 性格：外表冷酷、内心寂寞，自米爱出现后开始发生改变。 深爱的人：米爱 身高：183CM 身体状况：先天性心脏病 家世：尹家不是普通的有钱有势，据说省里的高官们都不敢轻易得罪他母亲。因为如果尹家离开，那么本省就会损失巨额财政来源。 外貌：耀眼的亚麻色头发，帅气的背脊，修长有力的双腿，一看就是很有力气的样子。 一个满脸恼怒的男生站在门口，他又高又帅，亚麻色头发，黑色T恤，浅蓝牛仔裤，鼻翼钉着一枚细细的钻石。 亚麻少年浑身被雨水打得湿透，眼底满是恼怒。水珠顺着他的头发狼狈滑落，脚上的雪白乔丹运动鞋也被泥水溅污得不成样子。 尹堂曜的面容十分帅气，鼻梁很挺很窄有一点弯弯的弧度，象古旧照片里倨傲的英国贵族，他的眉毛很浓，在梦里也坏脾气地皱着。真是好看的男孩子啊，鼻翼上的钻石让他多了些桀骜不逊的邪气，却也多了些难以捉摸的味道。 尹堂曜的脸绯红绯红，鼻翼钻石闪着动人的光芒，他眼底的恼意闪闪亮亮，察觉到她的偷看后，他更加用力地瞪她一眼。 为了小米,头发已经从亚麻色染回了黑色，初见他时他身上那种桀骜不逊任性嚣张的气焰也已经消失了，他的背影只是沉默而冰冷，只是孤独而寂寞。 好一个令人疼惜和爱怜的美少年啊!他时时刻刻都是那么的俊美,为了小米,他变了太多,穿白衬衣,上课不迟到,早退,上课不和老师顶嘴,按时完成作业!爱,已经让他疯狂了! 并录制同名歌曲《会有天使替我爱你》 2008 中国电影《娜娜的玫瑰战争》（扮演男主角洛阳） [编辑本段] MV演出 主演TAKE MV《化蝶》 主演TAKE MV《一句话》 友情出演中国歌手程怡MV《范儿》 友情出演林心如新歌MV《我以为我没有看见就好》 [编辑本段] 综艺节目 湖南卫视《勇往直前》《快乐大本营》《天天向上》《智勇大冲关》 山东电视台《天丰阳光快车道》 吉林卫视《超级乐8点》 BTV2《喜来坞》 河北卫视《综艺传奇》 上海人文艺术《世博会倒计时600天晚会》 湖北卫视《音乐集结号》 江苏综艺《非常大明星》 安徽卫视《剧风行动》 CCTV4《中华情欢聚常熟服装城》 2008年参加上海东方卫视举办的《梦圆东方》跨年晚会 云南卫视《智赢天下》上海录制 2008年参加湖南卫视举办的《舞动奇迹》，搭档：胡定欣 舞动奇迹 「歌词」T-ARA中文音译歌词大合集 T-ARA -- 好人 苦代噶求哇哈偶苏里够搜 苦带哇航给哈扣累(儿)扣罗(儿)哟 无哈你拉都难马金奇大谬 难破林girl 大奴(儿)几谬护卫哈拉库~~~ 奶满撒浪恩就奴果把赌九五撒浪你 苦带忒系内内kiao忒搜拿欺kiao就木里苏尼 某嘟土内给汗布kei木一内有 苦内扑奶撒浪撒浪给恰堆你一来哟 哭撒浪内给票塞尼亚塔底li几马 土带阿扑gi怕拉(尼恩)得撒浪你米噶 难吗啪啦布鲁奶一恰开萨拉米奈路 裤带忒细黑撒拉嗯大谬安giao叫（儿）不一苏你某嘟土内给汗布kei木一内有 苦内扑奶撒浪撒浪给恰堆你一来哟 哭撒浪内给票塞尼亚塔底li几马 土带阿扑gi怕拉(尼恩)得撒浪你米噶 度跟斗哈铺给含木看带末苏破名搜 my破林够五为哈谬乌谬撒拉卡gi~~ 苦土一艘拖塔斯某你婆里就~~ 裤袋啊配给卡雷嫩内乃卡都阿帕哟~ 汗布哈嘎度喊普罗库罗素hi怕古 T-ARA -- 好人 苦代噶求哇哈偶苏里够搜 苦带哇航给哈扣累(儿)扣罗(儿)哟 无哈你拉都难马金奇大谬 难破林girl 大奴(儿)几谬护卫哈拉库~~~ 奶满撒浪恩就奴果把赌九五撒浪你 苦带忒系内内kiao忒搜拿欺kiao就木里苏尼 某嘟土内给汗布kei木一内有 苦内扑奶撒浪撒浪给恰堆你一来哟 哭撒浪内给票塞尼亚塔底li几马 土带阿扑gi怕拉(尼恩)得撒浪你米噶 难吗啪啦布鲁奶一恰开萨拉米奈路 裤带忒细黑撒拉嗯大谬安giao叫（儿）不一苏你某嘟土内给汗布kei木一内有 苦内扑奶撒浪撒浪给恰堆你一来哟 哭撒浪内给票塞尼亚塔底li几马 土带阿扑gi怕拉(尼恩)得撒浪你米噶 度跟斗哈铺给含木看带末苏破名搜 my破林够五为哈谬乌谬撒拉卡gi~~ 韩国十大女子组合排行榜 以下排行榜是由30名韩国Entertainment Reporters（娱乐记者）排出的，可信度比较高，值得喜欢韩国少女组合的歌迷关注一下！ 本排名由“新乐网”细心整理，喜欢高清mv和高清演唱会的歌曲请关注一下！ 新乐高清网专注音乐视听新体验！- https://www.doczj.com/doc/ea11610726.html,/ 如果你觉得本文不错，请分享给你的朋友哦！ 排名按照知名度排名按照收入排名按照舞蹈技巧排名按照时尚触感排 名 总排名 1少女时代少女时代少女时代少女时代少女时代 2 Wondergirls 2ne1 2ne1 2ne1 2ne1 3 2ne1 brown eyed girls Afterschool brown eyed girls brown eyed girls 4 kara kara 4minute Wondergirls Wondergirls 5 brown eyed girls Wondergirls brown eyed girls 4minute t-ara 6 jewelry Afterschool Wondergirls kara 4minute 7 Afterschool jewelry kara Afterschool jewelry 8 4minute 4minute f(x) jewelry Afterschool 9 t-ara t-ara jewelry f(x) t-ara 10 f(x) (x) t-ara t-ara f(x) 纵使韩国娱乐业男子组合占了半边天，也是历来处于大势，但近年来女子组合奋勇直追，创造出了属于自己的一片天，也创造出很多大热HIT歌曲，就拿去年韩国最红的单曲中，几乎是女子组合的天下，取得了出色的业绩，风水轮流转，到底这些后浪，还能创造出什么样的成绩，我们拭目以待！ 韩国当今10大超人气女子组合详细介绍： 第一位：少女时代 》》》》》》》》》》》》》》》》》》》高清MV欣赏 少女时代为韩国娱乐公司S.M Entertainment在2007年推出的女子组合，由年龄为10代的9名女高中生组成。第一张单曲8月2日在韩国上市。出自艺人名门SM公司。 少女时代是韩国S.M Entertainment在2007年推出的由出道年龄全部为10代的9名女子高中生组合。第一张单曲《再次重逢的世界》2007年8月2日在韩国上市，2007年8月5日正式登台出道。出道时独特的宣传方法（每天公开一名成员），为她们以后的发展道路奠定了坚实的基础。通过成员们的不断努力，少女时代目前已成长为韩国顶尖的女子组合。出自艺人名门SM公司，少女时代当然不只是擅长唱歌跳舞的组合。她们经过的严格训练可能已经超过某些出道多年的艺人，SM娱乐公司表示：“少女时代这个组合名取自少女们主导的时 令人震惊的12位韩国最当红女明星真实资料 NO1. 宋慧乔 个人档案 生日：1982年2月26日 身高：164cm体重：45kg 血型：A型 学历：世宗大学电影艺术学系一年级 家庭成员：父母及她，无兄弟姊妹，家中还有二只狗 信仰：基督教 专长：游泳、钢琴及溜冰 兴趣：游泳 最喜欢的食物：火锅、汉堡、冰淇淋 最喜欢的电影：《情书》、《约定》 未来愿望：原本希望能成为一名服装设计师，但进入演艺圈后，则以演出文艺爱情剧的女主角为目前重心，而未来现实生活中，希望能是一位在家相夫教子的贤妻良母。 出道：1996年MTM（Model,Talent,Management）选拔大赛首奖 提到那个眉毛顺而密眼睛温而灵，嘴巴厚而润，而皮肤却无法概括形容的宋慧乔，不论男人还是女人都会得出这个结论——清纯。上帝制造她皮肤的时候一定加入了这些东西：两小时前刚刚盛开的玫瑰花瓣、用北海道超钙牛奶制成的牛奶豆腐，雪莹蚕的茧制成的上好雪缎和一点点olay净白莹采调理精华露 NO2. 蔡琳 姓名：CHAE RIM 原名：朴蔡琳 出生年月：1979年3月28日 身高：168cm 体重：48kg 三围：32-24-32 血型：A型 兴趣：钢琴、芭蕾 家庭关系：一男一女中的老大 学历：汉城艺专在校生 绰号：猩猩（身上毛很多） 首次亮相： 94年海太小姐选拔赛进入演艺圈，电视剧《热河》 演出作品： 《热河》、《挑战》、《女儿的选择》、《喝彩》、《伴侣》、《小小英雄们》、《妈妈的旗帜》、《女儿的选择》、《幸福在我心中》、《壮志凌云》 MBC-TV电视剧《害羞的恋人》 MBC-TV电视剧《跳跃运动员》 KBC-TV电视剧《迷失的鸟》 MBC-TV电视剧《亲爱的你》 韩国十大性感女团排行 Wonder GirlsWonder Girls Wonder Girls是5人女子团体，经纪公司为JYPEntertainment。以一首《Nobody》红遍大江南北。Wonder Girls因为大多时间在美国发展，导致在韩国的人气稍有下滑。 5、KaraKara KARA是韩国DSP公司2007年重点打造的新人女子组合，而曾经成功打造了红极一时的女子组合Fin.K.L的DSP 也有意再创一个女子组合的高峰时 代。KARA这个名字是取自于希腊语"CHARA",有着喜悦的意思，也希望通过KARA美妙的声音把喜悦带给大家。成员是由5位的可爱女生组 成分别是：队长朴奎利、主唱韩胜妍、Rapper郑妮可、领舞具荷拉以及老小姜智英(知英)。相比在韩国的人气，Kara 在日本的人气更高。 6、f(x)f(x) f(x)组合是由韩国SM娱乐有 限公司于2009年推出的5名女子偶像团体。f(x)组合成员包括队长宋茜Victoria(中国籍)、刘逸云 Amber(美籍华人)、郑秀晶Krystal(美韩双重国籍)崔雪莉Sulli、朴善怜Luna。2012年单曲《ElectricShock》大受欢迎，而中国成员宋茜在中韩两国也颇受欢迎。 7、Miss AMiss A miss A组合是韩国娱乐公司JYP Entertainment继Wonder Girls之后推出的第二支女子组合。miss A的前身是Sisters组合，miss A组合由两名中国成员孟佳、王霏霏，以及两名韩国成员裴秀智和李玟暎组成。 小编的女神也在这个组合，认识她主要是通过聚集了众多小鲜肉的偶像剧《dream high》。在这部集合了都教授金秀贤、2PM野兽玉泽演、牛奶cp张佑荣和IU众多韩国青春偶像的剧中。主演并演唱主题曲。(只可惜秀智和李敏镐现在谈恋爱了) 8、SISTARSISTAR 韩国比较火的女子组合 NO.1：少女时代 少女时代（Girls' Generation）是韩国著名的女子流行演唱组合，由金泰妍、郑秀妍、李顺圭、黄美英、金孝渊、权侑莉、崔秀英、林允儿、徐珠贤9 名成员组成。2007年8月初凭借首个团综《少女上学去》和出道单曲《再次重逢的世界》开始进入大家的视野，并迅速走红。其后更凭借《Gee》《Genie》《Oh!》《Hoot》等多首热门歌曲风靡大街小巷。2010年9月以一首《GENIE》进军日本乐坛。2011年10月中旬又以一首《The Boys》进军美国乐坛。自出道以来，少女时代就以优异的成绩改写了韩国女子组合乐坛在历史上的强大气势，并在中国、日本、泰国等亚洲国家和欧美等国家都累积出一定的知名度。她们除了作为歌手外，还以电视剧和音乐剧演员、电台DJ、节目MC和平面模特等身份活跃在演艺圈其他领域。曾连续两年被选入韩国福布斯名人榜首位，2009-2011连续3年韩国唱片总销量第一，在韩国乃至亚洲都拥有着超高的人气。 NO.2：T-ara T-ara，韩国女子偶像团体。拥有“韩国最百变女团”、歌谣界“变色龙”等美誉，由全宝蓝、李居丽、朴素妍、咸恩静、朴孝敏、朴智妍六名散发不同魅力的成员组成。组合名称T-ara，源自英文Tiara(王冠)，意指成为歌谣界的女王。T-ara以无限挑战特色风格的团队宗旨完美诠释了百变的无尽吸引力及超高风格驾驭力，如今不仅在歌谣界发光发热，更是在影视界、艺能界小有建树。出道四年，已成为韩国K-pop中心极具代表性的偶像团体之一。 No.3：2NE1 2NE1是韩国女子流行演唱团体，隶属于YG Entertainment公司，亦是经过4年时间准备的女子组合，被称为“女子bigbang”，是韩国年轻一代的潮流坐标。该组合由李彩琳（CL），朴春（Park Bom），朴山多拉（Sandara Park），孔敏智（Minzy ）。组成。组合名字借用21数字的英文发音，其中NE是New Evolution 的英文缩写，象征着2NE1这个组合在21世纪为韩国音乐带来新的进化。2009年5月6日以单曲《Fire》宣布正式出道，歌曲MV推出后仅一天的点击率就达到100万次，并迅速席卷各大音源排行榜的第一，成为韩国走红速度最快的女子组合。代表作品有《I Don…t Care》《Go Away》《Lonely》《Ugly》等。2013年7月8日，2NE1发行了最新单曲《Falling In Love》，华丽回归乐坛。 盘点韩国女团本尊颜值最美成员TOP5 本文导读：讲到韩国女团颜质的话，你觉得那一团平均质最高呢?谁是你心中的女神?日前韩国arirang TV就向115位偶像做了调查，选出他们心中「本尊最美的女团成员」!那废话不少说，赶紧来瞧瞧明星看明星谁才是第一名吧。 NO5. DAVICHI「姜珉耿」(???) ，得票率10% 姜珉耿，韩国二人女子组合Davichi成员之一。鹅蛋脸配双大眼睛，外加修长美腿&好歌喉...天籁女神现身怎能不印像深刻~ NO4. Apink「孙娜恩」(???) ，得票率15% 孙娜恩是韩国女子音乐组合Apink的六名成员之一，担任演唱及形象，通过试镜进入A Cube娱乐练习后成为Apink 的成员。春春美少女+优雅小女人的气质，光是远望也粉幸福，更何况是本人 NO3. f(x)「Krystal」，得票率19% 郑秀晶，艺名：Krystal是一名美韩双籍的女歌手、舞者、演员及主持人，为韩国女子团体f(x)成员之一，其亲姐姐为韩国女子团体少女时代成员Jessica。冷豔与可爱的衝击，要人怎能不动心呢~ NO2. miss A「秀智」(??) ，得票率23% 裴秀智，艺名为Suzy，是一名韩国女歌手、舞者及演员，女子流行音乐团体miss A成员之一。2010年7月1日以四人女子团体miss A在韩国出道，担任主唱，与Min同为团内韩国籍成员。精緻五官，配上时而淘气时而妩媚的魅力... 连敏镐欧巴都沦陷了，何况是他人呢~ NO1. 少女时代「林润妸」(??) ，得票率32% 林润妸，艺名：润娥，韩国女歌手、演员及主持人，2007年以女子团体少女时代出道。在队内担任副唱、主领舞。由于润娥通常会被安排在少女时代中间位置，因此有“少女时代的中心”之称，也就是少女时代的门面担当及形象担当，润娥亦有“国民理想型”之称。除了少女时代外，润娥在电视剧亦取得不俗的成绩。清新秀气的五官，配上甜美微笑，一眼就让人难忘记那美丽的身影... 韩国十大舞王舞后排名 1.第一代:具俊晔(他所在的酷龙组合在那个年代很有名，无人能及, 是他把街舞引进韩国的原祖~~）强调本人一点都不喜欢这个光头！ 2.第二代:张佑赫（他就更不用说了吧，当时的H.O.T可是震惊世界了，和杰克逊通台演出过哦.个人认为是真正的舞王~~主机械舞） 3.第三代:郑允浩（东方神起队长郑允浩，很帅，跳舞也很棒，现在的神起，可是亚洲第一天团，下一步就要走向世界.个人认为是新一代舞王~主Locking）俊秀（也不错的~~） 4.南贤俊（知名舞者~机械舞很好~~注重技巧~） 5.李珉宇（和佑赫属同一等级的吧（舞蹈）~~主街头舞蹈~）JUNJIN ，ERIC （都是不错的~） 6.金正南（机械舞也是很不错的~甚至有人说在佑赫之上~但我认为各有千秋） 7恩赫，东海（SJ成员~和允浩同属一辈~舞蹈也很不错~） 8.SEVEN （舞蹈也很不错~~） 9.Rain（舞蹈也不错~~但个人更喜欢看他演的电视剧~） 10.金钟民（虽然经常跳搞笑舞蹈~但是他舞蹈真的不错~个人很喜欢他的滑步） 以下纯属个人意见，仅供参考： TOP1:张佑赫（不用想了我心中的神...） TOP2:东方神起允浩（这个不用我说也知道吧，迟早他会超越我的神的。） TOP3:神话李珉宇（刚刚出道的时候就是觉得他帅，不过他现在跳的超好尤其喜欢他的337哦~~） TOP4:神话JUNJIN （...有力度的男人，成功源于努力。） TOP5:东方神起俊秀（看过他的激光舞没有？没看过？快去看看吧，那就是魔法） TOP6:SJ恩赫(看到SJ的舞蹈就知道了) TOP7:安七炫（个人觉得KANGTA还是唱情歌的时候最帅，不过舞跳的还真好） TOP8:FLY TO THE SKY fany（他的街舞可不盖的哦！随随便便来个TOMAS 吓死你！） TOP9:SEVEN （努力努力再努力，人气就是这么来的。） TOP10:RAIN （说实话我不觉得他有什么好，不过不要PIA我，他认真跳的舞还是很好的）还有女的哦~ 1.复古女王:裴瑟琪(这个不用我说了吧,她的复古舞真的太赞了) 2.李孝莉（这个偶啥也不说了，全人类都承认的。） 韩国14位最美女歌手_排行榜 14、闵孝琳 闵孝琳，1986年2月5日出生于韩国大邱，歌手、演员。在2009年的电视剧《Triple》中出演了一名花样滑冰的运动员；出版个人专辑《rinz闵孝琳》及单曲《Touch me》。2010年主演网络电影《爱上浪漫，首尔》。2011年参演电影《随风而逝》；同年参演电影《阳光姐妹淘》。2012年主演喜剧电影《五百万美元的男人》。2017年12月18日，确认于2018年2月与韩国男团BIGBANG成员太阳结婚。 13、李贞贤 李贞贤（···Lee Jung Hyun），1980年2月7日出生于韩国首尔市，毕业于韩国中央大学电影系，韩国影、视、歌多栖女艺人。1996年以演员身份参演电影《花瓣》成名，夺得“大钟奖”、“青龙奖”等多个新人演员奖项。1999年发行首张专辑《Let’s gO to My Star》，凭借歌曲《哇》、《换掉》在歌坛走红。 12、宝儿 演员、词曲创作者，韩国SM娱乐有限公司旗下艺人、理事。2000年，发行第一张专辑《ID Peaceb》。2004年，发行日本第十二张单曲《BeTheOne》，获得第11届大韩民国演艺艺术奖颁奖典礼获得新世代歌手大奖、韩流热潮功劳奖。2005年，首张日文精选《BEST OF SOUL》获得United World Chart （世界联合排行榜）第一名。 11、李艺真 李艺真（Ailee），1989年5月30日出生于美国科罗拉多州丹佛市。韩国女歌手、演员。2012年，1月参演校园剧《Dream High2》。2月发行数码单曲《Heaven》，正式出道。同年获得第14届Mnet亚洲音乐大奖最佳女子新人奖。2013年，1月获得第27届韩国金唱片大赏新人奖以及第22届首尔歌谣大赏新人奖。7月发行第二张迷你专辑《A’s Doll House》，其主打曲获得第28届韩国金唱片大赏音源本赏。 10、金亚荣 金亚荣（YURA），1992年11月6日出生于韩国蔚山广域市，韩国女歌手，女子组合Girls Day成员。2010年10月，金亚荣作为第二期成员正式加入Girl’s Day。2012年3月21日，出演电视剧《秘密天使》。2014年6月，金亚荣参加MBC综艺《我们结婚了4》，凭借该综艺获得2014年MBC演艺大 韩国13个最好看的音乐节目_排行榜 13、Fantastic DUO 《FantasticDuo》是SBS推出的一档只需有手机便可以和韩国顶尖歌手们合唱，并且能够亲眼见到“我手中的歌手”诞生成为最完美合唱的双向互动音乐综艺节目。全炫茂将担任节目主持人，干脆利落的主持风格令人期待。另外，《FantasticDuo―我手中的歌手》接档《K-POP STAR 5》于2016年4月17日16点50分（韩国时间）首播，于2016年11月20日结束第一季。第二季于2017年03月26日18点20分（韩国时间）播出。 12、挑战千曲 《挑战1000曲》是作为嘉宾出演的多唱歌的sbs的节目。原来2008年4月春季改编，以《挑战1000曲一小节练歌房》节目部分变形。2009年8月16日从《挑战1000曲》再次节目变形。knn在过去的2010年6月开始到2012年11月11日为止，星期天早上8点开始，编制talk力量的关系，但没有广播2012年11月18日开始以《挑战1000曲》成为了重组。2014年6月结束了最后一期。 11、不朽的名曲第二季 不朽的名曲2（·····2）是KBS2的一档歌唱竞赛节目，每期邀请一位或多位传说级前辈歌手，由偶像团体的主唱或solo歌手后辈翻唱其经典老歌并进行两两对抗，由现场观众投票选出优胜者，最终所有歌手演唱结束后产生最终本场最优歌手。 10、Super star K 《Super star K》是韩国有史以来最成功的音乐选秀节目，由金成柱等人主持，每周五晚11点在Mnet电视台播出，该节目通过在韩国全国的海选，诞生出《Super star k》的TOP 10选手来登上现场直播的评比舞台，通过激烈的评选比赛，让最具实力、最具才艺的选手脱颖而出。Mnet举办的歌唱比赛节目，韩国版的“American Idol”，比赛在全韩国八个城市进行，最后获胜除了可以得到一纸唱片合约之外，还可以获得一亿韩元的奖金（第三季已经增长到5亿韩元）。 9、Show Me The Money 《Show Me_The Money》是自2000~2004年< Hiphop the vibe >之后，时隔十多年Mnet电视台在2012年开始制作的一档发掘有实力的Hip-Hop 歌手的淘汰制节目，因被称为rap版<我是歌手>受到关注。《Show Me_The Money》是由传统hiphop本祖MC Meta带领的Meta Crew，和天 韩国当红女子组合资料大全 一. 少女时代 1. 人数：9个人。 2. 职责： 金泰妍 ( 队长&主唱) 郑秀妍 ( 主唱) 李顺圭 ( 主唱) 黄美英 ( 主唱) 徐珠贤 ( 主唱&领舞) 金孝渊 ( 主领舞) 崔秀英 ( 领舞) 林允儿 ( 领舞) 权侑莉 ( 领舞) 3. 音乐专辑： ? 再次重逢的世界 ( 2007.08.03) ? Kissing You ( 2007.11.01) ? Baby Baby ( 2008.03.13) ? Gee ( 2009.01.05) ? Chocolate Love ( 2009.10.08) ? Genie ( 2009.06.29) ? Oh! ( 2010.01.28) ? Run Devil Run ( 2010.03.22) ? Hoot ( 2010.10.27) ? Mr.Taxi ( 2011.04.17) 4. 成员名片： 金泰妍 艺名：泰妍 生日：1989.03.09 星座：双鱼座 身高：158CM 体重：45KG 血型：O 型 郑秀妍 艺名：jessica 生日：1989.04.18 星座：白羊座 身高：162CM 体重：43KG 血型：B 型 李顺圭 艺名：sunny 生日：1989.05.15 星座：金牛座 身高：158CM 体重： 42KG 血型：B型 黄美英 艺名：Tiffany 生日：1989.08.01 星座：狮子座 身高：163CM 体重：48KG 血型：O型 金孝渊 艺名：孝渊 生日：1989.09.22 星座：处女座 身高： 160CM 体重：48KG 血型：AB型 权俞利 艺名：Yuri 生日：1989.12.05 星座：射手座 身高：167CM 体重：48KG 血型：AB型 崔秀英 十大经典韩国爱情电影强烈推荐 韩国的浪漫爱情电影总是那么让人回味！男女主角们演绎一个个童话般纯洁的爱情故事，使得观众们沉浸在爱情的海洋里！ 一、菊花香 推荐等级：★★★★★ 也许，你闻见一个女子带着菊花香，就决定去爱她一生；也许，你随意地挑选出一首老歌，来倾述你的心声；也许，你会在树干上镌刻爱人的名字，让爱情生长；也许，你在生命的最后时刻，踩着沙沙作响的银杏叶子跳一支舞；也许，你亲手去做陶器，让幸福成为永恒的艺术--这些就是散发着 菊花香的故事。 二、那小子真帅 推荐等级：★★★★★ 主角是极具个性的美少年和平凡的灰姑娘，台词同样具有令人捧腹大笑的威力，描绘的也是一段至纯的情感。这样的白马王子和灰姑娘的故事总能让广大的女性观众大呼过瘾。 三、我的野蛮女金刚 推荐等级：★★★★★ 《我的野蛮女金刚》讲的是张娜拉如何以她的软功，把她爱上的男子慢慢地弄到手。看到她丰富的表情，听到她尖厉的说话声，还有类似跆拳道高手的身手！ 四、韩国情书 推荐等级：★★★★★ 韩国电影史上最美的影片《韩国情书》描绘了至死不渝的爱情承诺讲述一个童话般纯洁的爱情故事，是韩国巨星金喜善沉寂一年多后的倾力之作。 五、汉城假期 推荐等级：★★★★ 这是一部反映都市年轻爱情的影片。影片的拍摄没有一般爱情影片那种缤纷的色彩和青春的气息，而是通过色彩的深重反映了都市爱情颓废和脆弱的一面。影片充满了暧昧的味道，几位主演的演出也都可圈可点，不失为一部佳作！ 六、触不到的恋人 推荐等级：★★★ 触不到的时空，触不到的浪漫。空间能产生这种迷失感， 时间也同样可以。利用时间的扭曲来制造这种“擦肩而过”的效果似乎是最近一些韩国电影热衷的情节框架。 七、恋风恋歌 推荐等级：★★★★ 一部散文诗般的韩国电影，一个充满浓浓秋意的爱情故事，张东健在片中出演一个父亲身患重病的青年，帅气而忧郁。韩国三大美女之一的高小英在则出演一个导游小姐，两个人在风景如画的济州岛相遇相爱…… 八、八月照相馆 推荐等级：★★★★ 这是近年来极为少有的一部优秀影片，片中涉及到了世间所有的真情：朋友，爱人，家人，所以这是部适合所有人看的影片。 九、野蛮师姐 推荐等级：★★★★ 韩国当红艺人公司一览表 Mnet(韩国电视台) 所属公司： Mnet Media(大股东是日本CJ企划) 旗下股东子公司：Synnara Music 、 F&C Music 等 Mnet Media直属艺人： 李孝利 SG Wannabe SeeYa T-ara Davichi T-MAX ----------------------------------------------------------- Mnet Media子公司：Synnara Music 旗下艺人： 金钟国 孙丹菲 After School(现韩国平均身高最高女子组合) ---------------------------------------------------------- Mnet Media子公司：F&C music 旗下乐队： FTIsland C.N.Blue S.M Entertainment(韩国最大也是最富有争议的娱乐公司) 社长-李秀满 SM曲风：偏向舞曲，其中加以摇滚，爵士，Hip-Hop等多种音乐元素 旗下成功作品： 第一代：H.O.T(2001解散)、S.E.S(2002解散) 第二代：神话(2003跳巢)、BOA 第三代：东方神起(2009三只预脱离)、super junior(2009韩庚提出上诉) 、天上智喜 第四代：少女时代、SHINee(2010SM力捧) 第五代：F(x) PS:只有S.M公司有同时捧2个组合的财力,其他公司同一时期只捧1个组合.但其他公司旗下艺人都很和谐,未出现解约事件. YG Entertainment(韩国最大的HIPHOP公司) 由30名Entertainment Reporters排出的 知名度：1=少女时代2=Wondergirls 3=2ne1，4=kara，5=brown eyed girls，6=jewelry 7=Afterschool 8=4minute 9=t-ara 10=f(x) 收入：1=少时，2=2ne1，3=beg，4=kara，5=wg，6=as，7=jewelry， 8=4minute，9=t-ara，10=f(x) 舞蹈技巧：1=少时，2=2ne1，3=as，4=4minute，5=beg，6=wg，7=kara， 8=f(x)，9=jewelry，10=t-ara 时尚触感：1=少时，2=2ne1，3=beg，4wg，5=4minute，6=kara，7=as， 8=jewelry，9=f(x)，10=t-ara 总排名：1=少时，2=2ne1，3=beg，4=wg，5=kara，6=4minute，7=jewelry， 8=as，9=t-ara，10=f(x 纵使韩国娱乐业男子组合占了半边天，也是历来处于大势，但近年来女子组合奋勇直追，创造出了属于自己的一片天，也创造出很多大热HIT歌曲，就拿去年韩国最红的单曲中，几乎是女子组合的天下，取得了出色的业绩，风水轮流转，到底这些后浪，还能创造出什么样的成绩，我们拭目以待！ 韩国当今10大超人气女子组合： 一位：少女时代 少女时代为韩国娱乐公司S.M Entertainment在2007年推出的女子组合，由年龄为10代的9名女高中生组成。第一张单曲8月2日在韩国上市。出自艺人名门SM公司。 少女时代是韩国S.M Entertainment在2007年推出的由出道年龄全部为10代的9名女子高中生组合。第一张单曲《再次重逢的世界》2007年8月2日在韩国上市，2007年8月5日正式登台出道。出道时独特的宣传方法（每天公开一名成员），为她们以后的发展道路奠定了坚实的基础。通过成员们的不断努力，少女时代目前已成长为韩国顶尖的女子组合。出自艺人名门SM公司，少女时代当然不只是擅长唱歌跳舞的组合。她们经过的严格训练可能已经超过某些出道多年的艺人，SM娱乐公司表示：“少女时代这个组合名取自少女们主导的时代即将到来的含义，少女时代的成员们身兼歌手、电影演员、主持人、DJ等多种才能，而且成员们都擅长英语、汉语和日语的语言优势，除在韩国之外在全亚洲都将展开广泛的活动中文：少女时代韩文：????日文：しょうじょじだい英文：Girls' Generation ●关于应援：代表色：Pastel Rose 粉玫瑰色应援物：Pastel Rose Heart 粉玫瑰色心形气球应援口号：现在是少女时代，以后是少女时代，永远是少女时代！官方缩写：SOSI ●官方歌迷名称——S?NE 中文：夙愿粉玫瑰色应援气球 发音：So One含义：So代表少女时代，少女时代的韩文拼音是So Nyeo Shi Dae One代表歌迷齐心一致，少女时代和歌迷在一起Sone的O用心形来表示是代表少女时代爱自己的歌迷，并且少女时代的应援物为粉玫瑰色心形气球Sone一韩国十大当红女子组合
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