Diapositiva 1

1
THE NATURE OF DARK ENERGY FROM N-BODY
COSMOLOGICAL SIMULATIONS Paola
Solevi Università Milano - Bicocca
A.A. 2003/2004
2

• Overview of the talk
• What is Dark Energy?
• About n-body cosmological simulations
• How to constrain different DE models by n-body
  cosmological simulations Halos
  Profile
• Halos Mass function
• VPF
• ICL

3
What is Dark energy? The best fit model of WMAP
70 dark energy

• The cosmological constant is described by
  energy-momentum tensor
• Problems of LCDM cosmology
• Coincidence problem why just now ?
• Fine tuning

4

• Solution Dynamical Dark energy
• We have a real self-interactive scalar filed
  with a potential
• .
• Equation of motion
• Energy density
• Pressure
• Potentials which admit a tracker solution
• RP SUGRA

Where is the energy scale parameter.
5
The evolution of the DE density
of time vs. the scale factor
6

• Collision less n-body cosmological simulations
• All our simulations are performed using ART, a PM
  adaptive code (Klypin Kratsov) and QART,
  modification of ART (by Andrea Macciò) for models
  with DDE.
• PM (particle-mesh) calculation
• Assign charge to the mesh (particle mass
  grid density)
• Solve the field potential equation ( Poissons)
  on the mesh
• Calculate the force field from the mesh-defined
  potential
• Interpolate the force on the grid to find forces
  on the particles
• Integrate the forces to get particle velocities
  and positions
• Update the time counter

7
Basic ingredients Initial conditions power
spectrum of density perturbations depends on the
cosmological parameter inflationary model

n1 for scale-free HZ spectrum
is the transfer function (from CMBfast)
P(k) at z40 for different kind of Dark Energy.
8
Growing of perturbation depends on the background
evolution
Analytic formula for in Friedmann eq.


for resolving equations used in simulation
(eq. of Poisson ) (eq. of motion)
9
Linear features of the model Periodic
boundary conditions (homogeneity isotropy), we
need a large box for a good representation of the
universe Mass force resolution increase with
decreasing box size
Nrow number of particles in one dimension Lbox
box size Ngrid number of cells in one
dimension n number of refinment levels
10
Density profiles
All NFW profiles
but with different concentrations
FEATURES OF SIMULATED CLUSTERS RP3 LCDM SU3
Virial Radius (Mpc) 0.663 (149.8) 0.730 (103.1) 0.709 (118.3)
Virial Mass 5.01e13 4.44e13 4.53e13
Cvir 10.1 7.2 8.84
11
The best way for test different central
concentration is via Strong Gravitational
Lensing Formation of Giants Arcs More Arcs
for RP model
12
LCDM
Z0.3 Z0.5
Z1.0 Z1.5
RP
13
Mass function evolution
No differences predicted because of the same s8
normalization at But different
evolution expected z0
14
Void probability function Simulations run at
HITACHI MUNCHEN MPI 32 Node,32x256 Pr. Three
simulations LCDM, RP (?103GeV), SU (?103GeV)
Cosmologies Cosmologies Simulations features Simulations features
Om 0.3 LBox 100 h-1Mpc
ODE 0.7 Npart 2563
h 0.7 Mp 5.0x109 M?h-1
s8 0.90 ? 3.0 h-1kpc (7 refinement levels)
15
VPF is a function of all the correlation terms
- reduced n-point correlation function
mean value - mean galaxy number in VR Why do we
expect that VPF depend on the cosmological
model? Different evolution rate Different
halo PLCDM(R)gt PSU(R) gt PRP(R)

16
VPF, M gt 1x1012M?h-1
Z0.9
Z0
Just as for halos MF no differences predicted at
z0
But different evolution expected
17
VPF, M gt 1x1012M?h-1
VPF, M gt 5x1012M?h-1
Z1.5
Notice the dependences on the mass limit,
significant differences but halo number getting
low
18
Intracluster light ICL (intracluster light) is
due to a diffuse stellar component
gravitationally bound not to individual galaxies
but to the cluster potential. First ICL
Observations Zwicky 1951 PASP 63, 61 The
fraction of ICL depends on the dynamical state of
the cluster and on its mass so studying ICL is
important to understand the evolution of galaxy
clusters. ICL tracers Red Giants, SNIa,
ICGs,PNe Direct estimations of ICL surface
brightness are difficult because it is less than
1 of the sky brightness and because of the
diffuse light from the halo of the cD
galaxy. Origin -Tidal stripping -Infall
of large groups
19
Why PNe as ICL tracers? PN is a short (104
years) phase in stellar evolution between
asymptotic giant branch WD
(HR diagram)
Because of a so short life, studying PNes
properties is just like investigating mean local
features. The diffuse envelope of a PN re-emits
part of UV light from the central star in the
bright optical OIII (? 5007 Å) line.
Luminosity
Surface T
20

Hot central star T5x104K
Shell of gas from the envelope of central star
UV
(Arnaboldi et al 2003)
OIII emission
21
If metallicity is large emission on many
lines, scarce efficiency
Average efficiency 15
RELATIONSHIP OIII intensity metallicity
age of formation mass Pop I,
disk population poor
emitters Pop II, bulge population
strong emitters
Progenitor M Central Star M Progenitors birth PN type
2.4-8M? gt0.64M? 1 Gyr Type I
1.2-2.4M? 0.58-0.64M? 3 Gyr Type II
1.0-1.2M? 0.56M? 6 Gyr Type III
0.8-1.0M? 0.555M? 10 Gyr Type IV
22
Studying PNe, very low intensity stellar objects
are found Cluster materials outside galaxies can
be inspected Current studies concentrate on
Virgo Main danger in studying PNe
background emitters at ? 5007 Å contributing
25 of fake objects (interlopers)
Results - ICPNe not centrally
concentrated - 10 lt ICL lt 40
23

Numerical simulations aiming to reproduce the
observed PN distribution 1 Napolitano,
Pannella, Arnaboldi, Gehrardt,Aguerri, Freeman,
Capaccioli,Ghigna, Governato, Quinn,
Stadel 2003 ApJ 594, 172 PKDGRAV n-body
cosmological simulation, Model ?CDM, Om0.3,
s81, h0.7 Cluster of 3x1014M? (cluster
magnified, still n-body)
NO HYDRO
Np(ltRv) mp ?
5x105 5.06x108M? 2.5kpc
24
How to use DM to reproduce star
formation? Particle in overdensity hits
becomes a star - points with
at z 3, 2, 1, 0.5, 0.25, 0 Now for ICL
must trace unbound stars - trace points down to
z 0, reject those in subhalos cD What did
they do? - Phase space distribution analysis in
30x30 areas at 0.2, 0.4, 0.5, 0.6 Mpc from
cluster center - 2-p angular correlation
function - Velocity distribution along
l.o.s Consistency with observational data
25
2 Murante, Arnaboldi, Gehrardt, Borgani,
Cheng, Diaferio, Dolag, Moscardini, Tormen,
Tornatore, Tozzi ApJL 2004,
607, L83 GADGET (treeSPH) used for LSCS,
includes radiative cooling, SNa feedback,
star formation Model ?CDM, Om0.3, Ob0.019h-2,
s80.8, h0.7 117 clusters with M gt 1014M?h-1
HYDRO
mp,gas mp,DM ?
6.93x108M?h-1 4.62x109M?h-1 7.5 h-1kpc
26
Bound and free stars have been selected by SKID,
fraction depends on , optimal 20
h-1kpc Problems with spatial resolution
numerical overmerging causes apparently unbound
stars increasing resolution
Fraction of unbound stars gt 10
(Diemand et al 2003)
27
3 Willman, Governato, Wadsley, Quinn
astro-ph/0405094 and MNRAS 2004 (in
press) GASOLINE (treeSPH) includes
radiativeCompton cooling, SNa feedback, star
formation, UV background (HaardtMadau
1996) Cosmological simulation (n-body) 1 cluster
magnified Model ?CDM, Om0.3, Ob not given,
s81, h0.7
HYDRO
28
Coma-like galaxy cluster M 1.2x1015M?h-1
(Willman et al 2004)
Two large groups ranging in size from Fornax to
Virgo
29
NDM N mp,DM/M? mp, /M? ? / kpc
C2 6.9x105 8.5x105 1.5x109 7.2x107 3.75
C2,low 8.6x104 1.4x105 1.2x1010 8.3x108 7.5
Murante et al 6.6x109 10.8
Comparison of C2 with C2,low C2,low not enough
resolution
30
Bound and free stars were detected by SKID using
20 of stars found in
intracluster medium Problem stellar baryon
fraction 36 in simulation vs. 6-10 from 2MASS
SDSS data (Bell et al 2003). COOLING CRISIS
not enough effects to slow down star
formation Claim distribution of stars still
OK TRUE? Neglected effects could be star-density
dependent Is the sophisticated star formation
machinery really better than searching for
overdensity regions?
31
Various conclusions - Unbound stars fraction
depends on dynamical status of cluster
Two peaks at z0.55 and z0.2 correspond to the
infall of large groups Variation of IC stars
fraction from 10 at z1 to 22 at z0
(Willman 2004)
32
-More IC stars from large galaxies but more
star/unit-mass from small galaxies
IC fract.from halos MltM
(Willman et al 2004)
-85 of stars forms at z lt 1.1
Mass M
33

What did we do so far? ART its
generalization QART (modified for DE models)
Models ?CDM Om0.3, s80.75, h0.7
RP(?103GeV) Om0.3, s80.75, h0.7 Cluster with
M 2.92x1014M?h-1
Lbox Npart mpart ?
80 Mpc h-1 5123 3.17x108M?h-1 1.2 h-1kpc
Willman et al Willman et al 1.05x109M?h-1 2.6 h-1kpc
Napolitano et al Napolitano et al 3.54x108M?h-1 1.7 h-1kpc
34
LCDM z 0
35
RP3 z 0
36
LCDM z 1
37
RP3 z 1
38
LCDM z 2
39
RP3 z 2
40
Conclusions What are we doing? - Star
formation in iperdensities (SMOOTH), density
contrast to be gauged to reproduce observed
star amount - Star formation zs at ?z 0.1 -
Dynamical status of candidate-star particle
monitorized Extra aim Searching for
cosmological model dependencies due to -
different formation history - concentration of
dark matter halos