Florent Rostagni 1.  Context  Sample  Algorithm for detection and classification  Star formation  X-ray – optical study  Perspectives - Conclusion.

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Presentation transcript:

Florent Rostagni 1

 Context  Sample  Algorithm for detection and classification  Star formation  X-ray – optical study  Perspectives - Conclusion 2

t Big-Bang~ years Present Cosmic Microwave Background Galaxy distribution  T/T ~10 -5  /  ~ 200 GRAVITATION 3

STRUCTURES: FILAMENTS CLUSTERS > 100 galaxies M.  M  M. ~ 1 Mpc 4

structure formation rate cosmological parameters dynamical state of clusters 5

6 Cohn & White z z

7 Cohn et al. 2001

GALAXIES 1930 optical 5% of the mass GAS 1980 X-ray 15% of the mass DARK MATTER 1980 gravitationnal lens 80% of the mass 8

REGULAR CLUSTERIRREGULAR CLUSTER 9

 Projected galaxy disribution King profile 1 Mpc  Galaxy orbits gaussian velocity distribution 10

OPTICALX-RAY Galaxy contents Wavelets Adaptive kernel Centroid shift Power ratio elliptcity - projection effects - point distribution + whole cluster + no projection effect + continuous field - cluster core SKY PLANE 11

 Redshift information along the line of sight  SDSS 25% of the sky spec. complete up to r=17.5 best survey at low z available  C4 clusters: detected using 4 colors 12

 Search for concentration of galaxies in a 4 color space  Galaxy with 6-th nearest distance neighbour = C4 cluster center  All galaxies within r 15 are assigned to the cluster 13

 Results: 90% complete 95% pur 98% of X-Ray clusters 90% of Abell clusters 14

 Centering: Definition of the cluster center (6-th nearest neighbour in color space)  Piling up of structures along the line of sight: Due to only use of colors 1. Loss of some C4 clusters 2. Split of C4 clusters in several units 15

 What we have: 218 catalogs (fields) of galaxies around 10Mpc of C4 clusters with enough measured spectra  What we use: Position Photometric magnitude in r-band Spectroscopic (60%) and photometric redshift 16

 Search for galaxy concentrations in phase space 6 coordinates of galaxies  ( , ,z) only available 3D ( , ,z) ? 1D (z) + 2D ( ,  )  Construction of distribution functions based on wavelets + measure of deviations to relaxed ones 17

Fields z Fields z f(z) wavelets 18

Fields z Fields z f(z) wavelets Packets Peaks Ngal>50 EMMIX 19

22 20

21 Fields z Fields z f(z) wavelets Packets Peaks Ngal>50 EMMIX

22  156 peaks

List of peaks 2 cases distant peaks close peaks 23 POTENTIALLY RELAXED INTERACTION

24

Isolated or interacting peak ( ,  ) Isolated or interacting peak ( ,  ) Density maps ( 2-10Mpc) Density maps ( 2-10Mpc) Cluster catalog waveletsSExtractor 3Mpc density maps (0.5-3Mpc) 3Mpc density maps (0.5-3Mpc) wavelets Sub-stucture catalog SExtractor Ngal > Ngal max / Cluster catalog

3 cases 1 clump one main clump several heavy clumps POTENTIALLY RELAXED INTERACTION weight criteria: 1:10 26 Sub-stucture catalog

POTENTIALLY RELAXED POTENTIALLY RELAXED INTERACTION PEAKS POTENTIALLY RELAXED INTERACTION CLUMPS RELAXED INTERACTION 27

 218 C4 fields  110 packets 156 peaks  98 isolated peaks 216 clusters  29 isolated clusters 28 Isolated clusters rate ~ 13%

 Classification: relaxed interacting  Improvements: introduce ellipticity in classification interacting: elaborate sub-categories with bimodal

 Collaboration with H. Bourdin  Data for 18 C4 clusters with XMM  Galaxy: collisionless  Gas: collisional Different dynamical time Different morphologies Dynamical state + Fusion stage 30

31 OpticalX-Ray

 Temperature maps dynamical state, cold core 32

 What is the influence of cluster dynamical state on star formation rate of their host galaxies? Does cluster merging speed up star formation in galaxies or in contrary does it stop star formation?  Is it the case for all galaxies?  If not, where are star forming galaxies and/or star formation stopping galaxies? 33

 Galaxy type determined by spectra: Unclassified or not used Star forming Composite AGN Low S/N liner  Fraction of SF galaxies in clumps 34

 Classification of galaxy clusters  ~13 % isolated clusters 35

 Use of z phot : Impact of its use on classification, control of errors  Application of classification to CFHT-LS data: more galaxies deeper Evolution of fusion rate with time 36

37