Superclusters-Void Superclusters-Void
Network Network
Jaan Einasto
Jaan Einasto and and Enn Saar Enn Saar Tartu Observatory
Tartu Observatory
Bernard60 – 27.06.2006
Bernard60 – 27.06.2006
Early studies (1970 – 1980) Early studies (1970 – 1980)
Dark matter is dynamically dominating in the Dark matter is dynamically dominating in the Universe (1974)
Universe (1974)
Zeldovich question (1974) Zeldovich question (1974)
Structures evolve slowly – present structure close Structures evolve slowly – present structure close to initial
to initial
Search for structures using 1 Search for structures using 1
ststcomplete redshift complete redshift surveys
surveys
Virgo & Perseus superclusters – chains of galaxies, Virgo & Perseus superclusters – chains of galaxies, clusters, groups & voids
clusters, groups & voids
Density evolution in over- and under- dense regions:
In over-density regions density increases until collapses to form galaxies, clusters (pancaking);
In under-density regions density decreases and matter remains in primordial form (DM + rarefied baryonic matter)
Conclusion: galaxies & clusters form along chains in Conclusion: galaxies & clusters form along chains in superclusters, in low-density regions (voids) matter superclusters, in low-density regions (voids) matter
remains in primordial non-clustered form – remains in primordial non-clustered form –
first evidence for physical biasing (1980)
first evidence for physical biasing (1980)
Jim Peebles & Scott Tremaine 1977 Yakov Zeldovich & his wife, 1980 Zeldovich (speech on Tallinn conference banquet 1981):
Observers work hard in sleepless nights to collect data; theorist
interpret observations, are often in error, correct their errors and try again; and there are only very rare moments of clarification. Today it is one of such rare moments when we have a holy feeling of
understanding secrets of the Nature. Non-baryonic dark matter is needed to start structure formation early enough.
Tallinn conferences Tallinn conferences
1977: Large-Scale Structure of the Universe 1977: Large-Scale Structure of the Universe
1981: The Nature of Dark Matter
1981: The Nature of Dark Matter
Supercluster-void network: 2dFGRS data Supercluster-void network: 2dFGRS data
2dFGRS Northern and Southern regions contain superclusters of
various richness and voids
Supercluster-void network: SDSS DR4 Supercluster-void network: SDSS DR4
3 wedges ~10 deg wide in the DR4 high-declination zone
Structure of Structure of supercluster supercluster
SCL126 (Sloan SCL126 (Sloan
Great Wall) Great Wall)
Above: density field smoothed with 1& 8 Mpc/h kerner,
1 Mpc/h thick
Below: density field smoothed with
1 Mpc/h kernel,
~ 10 Mpc/h thick
Supercluster catalogues Supercluster catalogues
2dFGRS and SDSS DR4 have been used to find superclusters as high-density regions smoothed with Epanechnikov kernel of radius 8 Mpc/h. Luminosity density field has been corrected to take statistically into account galaxies and groups outside the observational window of the flux-limited galaxy catalogue 2dFGRS – 544 superclusters
SDSS DR4 – 911 ”
For comparison we used Millennium Simulation mock galaxy catalogue and found
Mill.A8 - 1733 superclusters (full sample)
Mill.F8 - 1068 ” (simulated 2dF sample)
Comparison of superclusters with models Comparison of superclusters with models
The distribution of maximal, minimal and effective diameters of
superclusters.
Left top: 2dF superclusters
Right top: Millennium superclusters
Right bottom: SDSS superclusters
Luminosity and multiplicity functions Luminosity and multiplicity functions
of superclusters of superclusters
Left: relative luminosity functions of superclusters (luminosity of poor superclusters is taken as unit)
Right: multiplicity functions of superclusters
Note the difference between real and simulated superclusters: luminosities and multiplicities of most luminous real superclusters exceed those of simulated superclusters about 5 – 8 times
Illustration of the difference of supercluster richness:
left – DM model M500; right – 2dFGRS North
Real Universe has more very rich supeclusters than predicted by
current models.
Wavelet decomposition of Wavelet decomposition of
the SDSS Density Field the SDSS Density Field
We use Data Release 3 of Sloan Digital Sky Survey.
Density field is calculated for Northern equatorial slice 2.5° thick, over 100° wide. Total expected luminosity is estimated assuming Schechter luminosity function.
Density field is smoothed using Gaussian kernel of size 0.8 Mpc.
To see the role of waves of different scale we use the ‘a trous wavelet transform.
The field is decomposed into several frequency bands, each band
contains frequencies twice the previous band. The sum of these bands
restores the original field.
Characteristic scale 512 256 128
64 32 Original
Results of the wavelet analysis Results of the wavelet analysis
Superclusters form in regions where large density Superclusters form in regions where large density waves combine in similar high-density phases
waves combine in similar high-density phases
Superclusters are the richer the larger is the Superclusters are the richer the larger is the wavelength of phase synchronization
wavelength of phase synchronization
Voids form in regions where large density waves Voids form in regions where large density waves combine in similar low-density phases
combine in similar low-density phases
Conclusions Conclusions
Structure of superclusters is very well explained by Structure of superclusters is very well explained by current models
current models
But: there are more very rich superclusters than But: there are more very rich superclusters than models predict
models predict
Large perturbations evolve very slowly and Large perturbations evolve very slowly and represent the fluctuation field at the epoch of represent the fluctuation field at the epoch of inflation
inflation
The difference between observations and models The difference between observations and models can be explained in two ways:
can be explained in two ways:
–
Large-scale perturbations are not incorporated in models, Large-scale perturbations are not incorporated in models, i.e. Models need improvement
i.e. Models need improvement
–
