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L. Perivolaropoulos leandros.physics.uoi.gr Department of Physics University of IoanninaPowerPoint Presentation

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ΛCDM: Triumphs, Puzzles and Remedies

L. Perivolaropouloshttp://leandros.physics.uoi.gr

Department of Physics

University of Ioannina

The consistency level of LCDM with geometrical data probes has been increasing with time during the last decade.

There are some puzzling conflicts between ΛCDM predictions and dynamical data probes (bulk flows, alignment and magnitude of low CMB multipoles, alignment of quasar optical polarization vectors, cluster halo profiles)

Most of these puzzles are related to the existence of preferred anisotropy axes which appear to be surprisingly close to each other!

The axis of maximum anisotropy of the Union2 SnIa dataset is also located close to the other anisotropy directions. The probability for such a coincidence is less than 1%.

The amplified dark energy clustering properties that emerge in Scalar-Tensor cosmology may help resolve the puzzles related to amplified bulk flows and cluster halo profiles.

Obs

GRB

Direct Probes of the Cosmic Metric:

Geometric Observational Probes

Direct Probes of H(z):

Luminosity Distance (standard candles: SnIa,GRB):

flat

Significantly less accurate probesS. Basilakos, LP, MBRAS ,391, 411, 2008arXiv:0805.0875

Angular Diameter Distance (standard rulers: CMB sound horizon, clusters):

Parametrize H(z):

Chevallier, Pollarski, Linder

Minimize:

WMAP3+SDSS(2007) data

ESSENCE (+SNLS+HST) data

Standard Candles (SnIa)

Lazkoz, Nesseris, LPJCAP 0807:012,2008.arxiv: 0712.1232

Standard Rulers (CMB+BAO)

Geometric Probes: Recent SnIa Datasets

Q1: What is the Figure of Merit of each dataset?

Q2: What is the consistency of each dataset with ΛCDM?

Q3: What is the consistency of each dataset with Standard Rulers?

J. C. Bueno Sanchez, S. Nesseris, LP, JCAP 0911:029,2009, 0908.2636

The Figure of Merit:Inverse area of the 2σ CPL parameter contour.A measure of the effectiveness of the dataset in constraining the given parameters.

GOLD06

SNLS

ESSENCE

UNION

CONSTITUTION

UNION2

WMAP5+SDSS7

WMAP5+SDSS5

The Figure of Merit:Inverse area of the 2σ CPL parameter contour.A measure of the effectiveness of the dataset in constraining the given parameters.

U2

C

SDSS MLCS2k2

U1

G06

SDSS SALTII

E

SNLS

SDSS5

Percival et. al.

SDSS7

Percival et. al.

Consistency with ΛCDM

Trajectories of Best Fit Parameter Point

ESSENCE+SNLS+HST data

Ω0m=0.24

SNLS 1yr data

The trajectories of SNLS, Union2 and Constitution are clearly closer to ΛCDM for most values of Ω0m

Gold06 is the furthest from ΛCDM for most values of Ω0m

Q: What about the σ-distance (dσ) from ΛCDM?

The σ-distance to ΛCDM

ESSENCE+SNLS+HST data

Trajectories of Best Fit Parameter Point

Consistency with ΛCDM Ranking:

The σ-distance to Standard Rulers

ESSENCE+SNLS+HST

Trajectories of Best Fit Parameter Point

Consistency with Standard Rulers Ranking:

From LP, 0811.4684,I. Antoniou, LP 1007.4347

Puzzles for ΛCDM

Large Scale Velocity Flows

- Predicted: On scale larger than 50 h-1Mpc Dipole Flows of 110km/sec or less. - Observed: Dipole Flows of more than 400km/sec on scales 50 h-1Mpc or larger. - Probability of Consistency:1%

R. Watkins et. al. , 0809.4041

Alignment of Low CMB Spectrum Multipoles

- Predicted: Orientations of coordinate systems that maximize of CMB maps should be independent of the multipole l . - Observed: Orientations of l=2 and l=3 systems are unlikely close to each other. - Probability of Consistency:1%

M. Tegmark et. al., PRD 68, 123523 (2003), astro-ph/0302496

Large Scale Alignment of QSO Optical Polarization Data

- Predicted: Optical Polarization of QSOs should be randomly oriented - Observed: Optical polarization vectors are aligned over 1Gpc scale along a preferred axis. - Probability of Consistency:1%

D. Hutsemekers et. al.. AAS, 441,915 (2005), astro-ph/0507274

Cluster and Galaxy Halo Profiles:

- Predicted: Shallow, low-concentration mass profiles - Observed: Highly concentrated, dense halos - Probability of Consistency:3-5%

Broadhurst et. al. ,ApJ 685, L5, 2008, 0805.2617, S. Basilakos, J.C. Bueno Sanchez, LP., 0908.1333, PRD, 80, 043530, 2009.

Three of the four puzzles for ΛCDM are related to the existence of a preferred axis

QSO optical polarization angle along the diretction l=267o, b=69o

D. Hutsemekers et. al.. AAS, 441,915 (2005), astro-ph/0507274

Quasar Align.

CMB Octopole

Q1: Are there other cosmological data with hints towards a preferred axis?

Q2: What is the probability that these independent axes lie so close in the sky?

CMB Dipole

I. Antoniou, LP 1007.4347

CMB Quadrup.

Velocity Flows

Octopole component of CMB map

Dipole component of CMB map

Quadrupole component of CMB map

M. Tegmark et. al., PRD 68, 123523 (2003), astro-ph/0302496

2. Evaluate Best Fit Ωm in each Hemisphere

Z=1.4

1. Select Random Axis

Z=0

3.Evaluate

Union2 DataGalactic Coordinates

(view of sphere from opposite directions

4. Repeat with several random axes and find

Anisotropies for Random Axes Union2 Data North-South Galactic Coordinates

ΔΩ/Ω=0.43

ΔΩ/Ω=

-0.43

Direction of Maximum Acceleration: (l,b)=(306o,15o)

Anisotropies for Random Axes (Union2 Data)

View from above Maximum Asymmetry Axis Galactic Coordinates

Minimum Acceleration: (l,b)=(126o,-15o)

ΔΩ/Ω=0.43

ΔΩ/Ω=

-0.43

Maximum Acceleration Direction: (l,b)=(306o,15o)

Consistency with Statistical Isotropy

Construct Simulated Isotropic Dataset:

2. Keep same directions, redshifts and σμi as real Union2 data

3. Replace real distance modulus μobs(zi) by gaussian random distance moduli with mean and standard deviation σμi .

1. Find best fit parameter and zero point ofcet

ΔΩ/Ω=0.43

ΔΩ/Ω=-0.43

Anisotropies for Random Axes (Isotropic Simulated Data)View from above Maximum Asymmetry Axis (Galactic Coordinates)

1. Compare a simulated isotropic dataset with the real Union2 dataset by splitting each dataset into hemisphere pairs using 10 random directions.

2. Find the maximum levels of anisotropy (Union2) and (Isotropic) and compare them.

3. Repeat this comparison experiment 40 times with different simulated isotropic data and axes each time.

Distribution of

Isotropic Data can Reproduce the Asymmetry Level of Union2 Data

Maximum after 10 trial axes

Frequencies

Max

Statistical Isotropy Test: Results

In about 1/3 of the numerical experiments (14 times out of 40)

i.e. the anisotropy level was larger in the isotropic simulated data.

There is a direction of maximum anisotropy in the Union2 data (l,b)=(306o,15o). The level of this anisotropy is larger than the corresponding level of about 70% of

isotropic simulated datasets but it is consistent with statistical isotropy.

Simulated Data Test Axes

Real Data Test Axes

ΔΩ/Ω=0.43

ΔΩ/Ω=0.43

ΔΩ/Ω=-0.43

ΔΩ/Ω=-0.43

Q: What is the probability that these independent axes lie so close in the sky?

Calculate:

Compare 6 real directions with 6 random directions

Q: What is the probability that these independent axes lie so close in the sky?

Simulated 6 Random Directions:

Calculate:

6 Real Directions (3σ away from mean value):

Compare 6 real directions with 6 random directions

Distribution of Mean Inner Product of Six Preferred Directions (CMB included)

The observed coincidence of the axes is a statistically very unlikely event.

8/1000 larger than real data

Frequencies

< |cosθij|>=0.72 (observations)

<cosθij>

Distribution of Mean Inner Product of Three Preferred Directions (CMB excluded)

Even if we ignore CMB data the coincidence remains relatively improbable

71/1000 larger than real data

Frequencies

< |cosθij|>=0.76(observations)

<cosθij>

Models Predicting a Preferred Axis Directions (CMB excluded)

- Anisotropic dark energy equation of state (eg vector fields)(T. Koivisto and D. Mota (2006), R. Battye and A. Moss (2009))
- Horizon Scale Dark Matter or Dark Energy Perturbations (eg 1 Gpc void)(J. Garcia-Bellido and T. Haugboelle (2008), P. Dunsby, N. Goheer, B. Osano and J. P. Uzan (2010), T. Biswas, A. Notari and W. Valkenburg (2010))
- Fundamentaly Modified Cosmic Topology or Geometry (rotating universe, horizon scale compact dimension, non-commutative geometry etc)(J. P. Luminet (2008), P. Bielewicz and A. Riazuelo (2008), E. Akofor, A. P. Balachandran, S. G. Jo, A. Joseph,B. A. Qureshi (2008), T. S. Koivisto, D. F. Mota, M. Quartin and T. G. Zlosnik (2010))
- Statistically Anisotropic Primordial Perturbations (eg vector field inflation)(A. R. Pullen and M. Kamionkowski (2007), L. Ackerman, S. M. Carroll and M. B. Wise (2007), K. Dimopoulos, M. Karciauskas, D. H. Lyth and Y. Ro-driguez (2009))
- Horizon Scale Primordial Magnetic Field.(T. Kahniashvili, G. Lavrelashvili and B. Ratra (2008), L. Campanelli (2009))

Cluster Halo Profiles Directions (CMB excluded)

Navarro, Frenk, White, Ap.J., 463, 563, 1996

NFW profile:

From S. Basilakos, J.C. Bueno-Sanchez and LP, PRD, 80, 043530, 2009, 0908.1333.

ΛCDM prediction:

The predicted concentration parameter cvir is significantly smaller than the observed.

Data from:

Cluster Halo Profiles Directions (CMB excluded)

Navarro, Frenk, White, Ap.J., 463, 563, 1996

From S. Basilakos, J.C. Bueno-Sanchez and LP, PRD, 80, 043530, 2009, 0908.1333.

NFW profile:

clustered dark energy

Clustered Dark Energy can produce more concentrated halo profiles

Data from:

________ ___ ______ ____________ Directions (CMB excluded)

Q: Is there a model with a similar expansion rate as ΛCDM but with significant clustering of dark energy?

A: Yes. This naturally occurs in Scalar-Tensor cosmologies due to the direct coupling of the scalar field perturbations to matter induced curvature perturbations

J. C. Bueno-Sanchez, LP, Phys.Rev.D81:103505,2010, (arxiv: 1002.2042)

_________ ___________ ________ Directions (CMB excluded)

Flat FRW metric:

Generalized Friedman equations:

These terms allow for superacceleration(phantom divide crossing)

Curvature (matter) drives evolution of Φ (dark energy) and of its perturbations.

_________ ___ __________ Directions (CMB excluded)

- Advantages:
- Natural generalizations of GR (superstring dilaton, Kaluza-Klein theories)
- General theories (f(R) and Brans-Dicke theories consist a special case of ST)
- Potential for Resolution of Coincidence Problem
- Natural Super-acceleration (weff<-1)
- Amplified Dark Energy Perturbations

Constraints:

Solar System

Cosmology

__ _______ _______ Directions (CMB excluded)

Oscillations (due to coupling to ρm ) and non-trivial evolution

________ _______ __ ____ Directions (CMB excluded)

Effective Equation of State:

Scalar-Tensor (λf=5)

weff

Minimal Coupling (λf=0)

z

____________ __ _____ ______ Directions (CMB excluded)

Perturbed FRW metric (Newtonian gauge):

J. C. Bueno-Sanchez, LP, Phys.Rev.D81:103505,2010, (arxiv: 1002.2042)

Anticorrelation

No suppression on small scales!

____________ __ _____ ______ Directions (CMB excluded)

Sub-Hubble GR scales

Suppressed fluctuations on small scales!

(as in minimally coupled quintessence)

________ ________ Directions (CMB excluded)

Scale Dependence of Dark Energy/Dark Matter Perturbations

Minimal Coupling (F=1)

Non-Minimal Coupling (F=1-λfΦ2)

Dramatic (105) Amplification on sub-Hubble scales!

Summary Directions (CMB excluded)

Early hints for deviation from the cosmological principle and statistical isotropy are being accumulated. This appears to be one of the most likely directions which may lead to new fundamental physics in the coming years.

The amplified dark energy clustering properties that emerge in Scalar-Tensor cosmology may help resolve the puzzles related to amplified bulk flows and cluster halo profiles.

________ ________ Directions (CMB excluded)

Scale Dependence of Dark Matter Perturbations

Minimal Coupling (F=1)

Non-Minimal Coupling (F=1-λfΦ2)

10 % Amplification of matter perturbations on sub-Hubble scales!

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