DEUS Consortium - University of Miami€consortium.org Jean‐Michel ALIMI Dark Energy Universe...

DARK ENERGY UNIVERSE SIMULATION

DEUS Consortium(J.‐M. Alimi, P.‐S. Corasaniti, Y. Rasera, V. Bouillot, V. Reverdy, I. Balmes, …)

www.deus‐consortium.orgwww.deus consortium.org

Jean‐Michel ALIMI

Dark Energy Universe Simulation

• The Cosmic expansion is accelerated• The Cosmic expansion is accelerated

• What is the nature of the Dark Energy that drives this acceleration ? Probably, the most challenging problem in Physics.

• How can we distinguish between DE Models ?

• What can we learn on DE from LSS Formation ?/

• How LSS formation process is affected by the presence of Dark Energy ?

Developping the largest cosmological DM simulations to date with realistic DEcomponent, involving billions of particules, highest spatial resolution for the largestp , g p , g p gset of simulated Universe, our challenge is to reproduce with unprecedented detailsthe cosmic structure formation process and answer to these fundamental questionsboth from theoretical point of view and by providing useful results for the presentand future cosmological surveys as SDSS Planck DES Euclid

Jean‐Michel ALIMI

and future cosmological surveys as SDSS, Planck, DES, Euclid, …

Dark Energy Universe Simulation

• Observational Evidences of Dark Energy• Nature of Dark Energy

• What can we learn on DE from LSS Formation ?

• How should we proceeded to perform numerical simulation of structure formation in presence of Dark Energy ?

− Realistic DE ModelsS f I l i N B d i l i− Software Implementation, N‐Body simulations

• Dark Energy Universe Simulation Series• DEUS in the international Context

• Numerous DEUS Chalenges and Results− DEUS series and Cosmological Survey− DEUS and Theoretical developments

Imprints of Dark Energy on the non linear matter power spectrum−Imprints of Dark Energy on the non‐linear matter power spectrum −Anomalous cosmic flow, a challenge for CDM −Imprints of Dark Energy on the halo mass function−In progress…

Jean‐Michel ALIMI

Observational Evidences of “Dark Energy”•• SNSN IaIa luminosity distance (SCP HST SNLS ESSENCE)luminosity distance (SCP HST SNLS ESSENCE)SN SN IaIa luminosity distance (SCP,HST,SNLS,ESSENCE)luminosity distance (SCP,HST,SNLS,ESSENCE)

•• CMB anisotropy power spectra (WMAP)CMB anisotropy power spectra (WMAP)

•• Matter P(k) (from 2dF and SDSS)Matter P(k) (from 2dF and SDSS)

••BAO (detected in 2dF and SDSS) , ISWBAO (detected in 2dF and SDSS) , ISW--correlation (several galaxy surveys vs. WMAP)correlation (several galaxy surveys vs. WMAP)Cluster Number Counts (Chandra detections) …Cluster Number Counts (Chandra detections) …

Jean‐Michel ALIMI

Observational Evidences of “Dark Energy”

The Concordance Model CDMBaryons ~ 5%

Cosmic complementarity

CDM ~ 25%

Radiations ~ 0.01%

Dark Energy ~ 70%

DarkDark MatterMatter ??DarkDark EnergyEnergy ??Kowalski et al. 2008

Jean‐Michel ALIMI

DarkDark MatterMatter ? ? DarkDark EnergyEnergy ??

Nature of Nature of “Dark EnergyDark Energy” ??

Cosmological ParadigmCosmological Paradigm Covariance Principle Equivalence Principle

Ga

Gak

aa

43

82

2

} Cosmological Principle PG

aa 3

34

New energy‐component () + to LM and DM:

Extensions to GR Extensions to GR The Hypothesis “Einstein’s General Relativity is the

if0 pa

Violation of the strong energy condition Geometrical and Dynamical Geometrical and Dynamical

Interpretation

General Relativity is the standard model of

gravitation” is conserved

The Cosmological Principle3

if 0 paExtensions to GR can be

interpreted as an extra energy‐component in a « quasi »

Standard Model and theoretical

The Cosmological Principle is discussed

New vision of the Universe:LT Solution Averaged q

Friedmann model ?

There are numerous proposed models of dark energy !

extensions LT Solution, Averaged

inhomogeneous Universes.

Jean‐Michel ALIMI

What can we learn on DE from LSS Formation ?

How can we discriminate between all Dark Energy Models ?How can we discriminate between all Dark Energy Models ?

Can Large Scale Structure settle the Dark Energy debate? New constraints on Dark Energy from Large Scale Structure Formation

Large scales (linear regime) / Small Scales (non linear regime)

gy g Criteria for detecting w(z) (at z>>1) Predictions on Large Scale Structure from alternatives to CDM

•• LSS tests: ISWLSS tests: ISW‐‐correction, Weak Lensing, BAO and Peculiar Velocitiescorrection, Weak Lensing, BAO and Peculiar VelocitiesLarge scales (linear regime) / Small Scales (non‐linear regime)

•• SSS: SSS: Effects could be negligible. Effects could be negligible. −− Specially in the case where only background evolution is modified as in Specially in the case where only background evolution is modified as in Quintessence Models. Quintessence Models. −−Mass functions (one can use Jenkins et al ‘01) and nonMass functions (one can use Jenkins et al ‘01) and non‐‐linear corrections to linear corrections to power spectrum do not differ significantly from power spectrum do not differ significantly from CDM (one can use Smith et CDM (one can use Smith et al ‘03) al ‘03)

Is it Correct ?Is it Correct ?Hi h f N i l Si l ti ld b U f lHi h f N i l Si l ti ld b U f l

Jean‐Michel ALIMI

High performance Numerical Simulations could be very Useful. High performance Numerical Simulations could be very Useful.

Numerical Simulations (CDM only, here) with corresponding H(a),…DM FIELD DM HALOS DM EVOLUTION

Numerical Simulations (CDM only, here) with corresponding H(a),…DM FIELD DM HALOS DM EVOLUTION

Constraints from observational

Constraints from observational Linear Matter Linear Matter

DM FIELD, DM HALOS, DM EVOLUTIONDM FIELD, DM HALOS, DM EVOLUTION

THEORETICAL INTERPRETATIONS

THEORETICAL PREDICTIONS AND

OBSERVATIONAL

THEORETICAL INTERPRETATIONS

THEORETICAL PREDICTIONS AND

OBSERVATIONAL

observational data (SNe Ia, CMB, BAO,… )

→ M (CDM,), Q 8

observational data (SNe Ia, CMB, BAO,… )

→ M (CDM,), Q 8

Power Spectrum at z=0 and

Linear growing modes D+(a) →

Power Spectrum at z=0 and

Linear growing modes D+(a) →

OBSERVATIONAL CONSTRAINTS …OBSERVATIONAL CONSTRAINTS …

Q, 8REALISTIC

COSMOLOGICAL MODELS

Q, 8REALISTIC

COSMOLOGICAL MODELS

INITIALCONDITIONS `

at zstart

INITIALCONDITIONS `

at zstart

Theoretical approaches to DE (QUINTESSENCE, Coupled Models, « AWE/ Non universal ST Gravity », f(R), …)

→ a(t), H(t), (t,x), D+(a) , G(t), …

Theoretical approaches to DE (QUINTESSENCE, Coupled Models, « AWE/ Non universal ST Gravity », f(R), …)

→ a(t), H(t), (t,x), D+(a) , G(t), …

Jean‐Michel ALIMI

Realistic Quintessence Models (RQM).

Cosmological constant CDM: 23

4m

Gaa

if0 mQQpa

QQm pGa 3

34

Quintessence scenari: DE as a Violation of the strong energy condition

3 if 0 Qpa QQm p

a 3

VQ 2² VpQ

2² 03

ddV

aa

2 2 da

Ratra-Peebles (1998) potential (SUSY breaking, backreactions, …) RPCDMSugra potential (radiative correction of RPCDM at E~mPl) SUCDM

V 4

V 4

exp 4G

Jean‐Michel ALIMI

RQM: From Observational Data to Cosmological Parameters

Lik lih d l i f th bi d SNI UNION d t t d WMAP5 d t

RPCDM SUCDMKlypin et al 2003

Likelihood analysis of the combined SNIa UNION dataset and WMAP5 data.Flat Universe, CAMB modified to take into account Q clustering.

RPCDM SUCDM

Maio et al 2006

Realistic Models (Alimi et al 2008)(Alimi et al 2008)

Dolag et al 2004

• Constraints onm et (,Q) from Union SNe Ia data set(Kowalski et al 2008)• Constraints onb,CDM,8 fromWMAP5 (Komatsu et al 2008).

C l i L l h2 li htl l th i CDM

Jean‐Michel ALIMI

Conclusion Low slope, mh2 slightly lower than in CDM

Realistic Quintessence Models: Cosmological parameters table

Parameters CDM RPCDM SUCDM

H0 (km/s/Mpc) 72 72 72

0 26 0 23 0 25cdm 0.26 0.23 0.25

bh2 0.02273 0.02273 0.2273

8lin 0.8 0.66 0.738

0 0.5 1

(eV) 2.4 x 10‐3 4.9 2.1 x 103

AS 2.1 x 10‐9 2.0 x 10‐9 2.1 x 10‐9

ns 0.951 0.951 0.951

Flat Uni erse 1

w0 ‐1 ‐0.87 ‐0.94

w1 0 0.08 0.19

Jean‐Michel ALIMI

Flat Universe ,Q=1-m

RQM: From Observational Data to Linear Matter Power Spectrum and Initial Conditions

CAMB modified to take into account Q clustering. Plin(k) including effect Q evoluated backward up to zstart with D+(z).

DE Clusterization

Different b/CDM

Different 8

• Realistic Quintessence and DE models are “equivalent” to explain CMB, Sne Ia, BAO…• Degeneracies of the models: Structuration as a discriminating test ?!

•CDM vs QCDM’s: frozen vs dynamical DERPCDM SUCDM i ( ) (/ t t)

Jean‐Michel ALIMI

•RPCDM vs SUCDM: varying w(z) (/w constant)

Software Implementation – Initial Conditions for N‐Body simulations

I iti l diti t MPGRAFIC QInitial conditions generator: MPGRAFIC_Q• Parallel version of GRAFIC (Prunet et al, 2008)• Gaussian random field and Zel’dovich approximation, Initial redshift deep in the linear regimeC l i l ti difi d f D k E t ki i t t Q Cl t i• Cosmological routines modified for Dark Energy taking into account Q Clustering.

DE Clusterization

Different b/CDM

Different 8

Jean‐Michel ALIMI

RAMSES Q: a fully threaded tree-based (Khokhlov 98) AMR code with PM solver (RAMSES, Teyssier, 2002)

Software Implementation ‐ High resolution N‐Body simulations

RAMSES_Q: a fully threaded tree based (Khokhlov 98) AMR code with PM solver (RAMSES, Teyssier, 2002)Cartesian mesh refined on a cell by cell basisocts: small grid of 8 cells, pointing towards

1 parent cell6 neighboring parent cells8 hild t8 children octs

Multigrid method for Poisson equationTime integration using recursive sub-cycling

Parallel computing using the MPI library, Domain decomposition using « space filling Peano‐Hilbert curves », (Gadget)Good scalability up to 32768 (BlueGene/P) processorsM ti i ti iMemory optimisation in every proc.Cosmological routines modified for Dark Energy

Jean‐Michel ALIMI

Dark Energy Universe Simulations Series: A large set of simulations

Large set of Universe Volumes (+ 25 simulations)Large set of Universe Volumes (+ 25 simulations), Very High spatial resolution: 2.5 h‐1 kpc to 10.4 h‐1Gpc, Very High mass resolution: 2.5 1010 h‐1 M⦿ to 1016 h‐1M⦿

HalosHalosInitial redshift deep in linear regime

Jean‐Michel ALIMI

Dark Energy Universe Simulations SeriesLARGE SET OF UNIVERSE VOLUMES (+ 25 SIMULATIONS),

Box Size Force Mass Number Initial Cosmologica Calculateur

LARGE SET OF UNIVERSE VOLUMES (+ 25 SIMULATIONS), HIGH SPATIAL RESOLUTION AND MASS: 2.5 h‐1 kpc to 10.4 h‐1 Gpc, 2.5 108 h‐1 M⦿ to 1016 h‐1M⦿

INITIAL REDSHIFT DEEP IN LINEAR REGIME

Resolution Resolution of Particles

Redshiftg

l Models (Nb of Proc)

162 h‐1 Mpc 2.5 h‐1 kpc ~2. 109 h‐1 M⦿ 5123 ~90 Λ1_1,SU1 1,

Titane (64)SU1_1, RP1_1

162 h‐1 Mpc 2.5 h‐1 kpc ~2.5 108 h‐1 M⦿ 10243 ~130 Λ1_2, SU1_2, RP1_2

Blue Gene/P(4096)

648 h‐1 Mpc 20 h‐1 kpc ~1.5 1011 h‐1 M⦿ 5123 ~55 Λ4_1,SU4_1, RP4_1

‐

648 h‐1 Mpc 10 h‐1 kpc ~1.75 1010 h‐1 M⦿ 10243 ~90 Λ4 2 Blue648 h Mpc 10 h kpc 1.75 10 h M⦿ 1024 90 Λ4_2, SU4_2, RP4_2

Blue Gene/P(4096)

648 h‐1 Mpc 5 h‐1 kpc ~2. 109 h‐1 M⦿ 20483 ~90 Λ4_4, RP4 4

Blue Gene/P(3276

Jean‐Michel ALIMI

_ / (8)

Dark Energy Universe Simulations SeriesLARGE SET OF UNIVERSE VOLUMES (+ 25 SIMULATIONS),

Box Size Force Mass Number Initial Cosmological Calculateur

LARGE SET OF UNIVERSE VOLUMES (+ 25 SIMULATIONS), HIGH SPATIAL RESOLUTION AND MASS: 2.5 h‐1 kpc to 10.4 h‐1 Gpc, 2.5 108 h‐1 M⦿ to 1016 h‐1M⦿

INITIAL REDSHIFT DEEP IN LINEAR REGIME

Resolution Resolution of Particles

Redshift Models (Nb of Proc)

1296 h‐1 Mpc 40 h‐1 kpc ~1. 1012 h‐1 M⦿ 5123 ~40 Λ8_1,SU8_1, ‐_ ,RP8_1

2592 h‐1 Mpc 40 h‐1 kpc ~1. 1012 h‐1 M⦿ 10243 ~55 Λ16_2, SU16_2, RP16_2

Blue Gene/P(4096)

2592 h‐1 Mpc 20 h‐1 kpc ~1.5 1011 h‐1 M⦿ 20483 ~55 Λ16_4, RP16_4

Blue Gene/P(24576

)

5184 h‐1 Mpc 40 h‐1 kpc ~1. 1012 h‐1 M⦿ 20483 ~40 Λ32 4, Blue5184 h Mpc 40 h kpc 1. 10 h M⦿ 2048 40 Λ32_4, RP32_4

BlueGene/P(24576

)

10368 h‐1 Mpc 40 h‐1 kpc ~1. 1012 h‐1 M⦿ 40963 ~40 Λ64_8 Curie Fat Nodes (9728)

Jean‐Michel ALIMI

( )

20736 h‐1 MpcFull Universe

RUN

40 h‐1 kpc ~1. 1012 h‐1 M⦿ 81923 ~100 Λ128_16RP128_16W’128_16

Curie ThinNodes (80 000)

Dark Energy Universe Simulation (Mass Resolution)

Jean‐Michel ALIMI

Dark Energy Universe Simulation Series (Spatial Resolution)

2.5 kpc/h 10 4 Gpc/h2.5 kpc/hΛCDM

5 kpc/h 500 Mpc/hMillenium I8 kpc/h Horizon 4pi 2 Gpc/h

6.6 Gpc/h160 kpc/h Horizon Run

10.4 Gpc/h

2.6 Gpc/hSugra25 kpc/h 260 Mpc/hCasarini et al, 2009

1 5 G /hJ i t l 2009

4.3 Gpc/h

6.6 Gpc/h160 kpc/h Horizon RunMillenium XXL 10 kpc/h

Ratra‐Peebles50 kpc/h 1.5 Gpc/hJennings et al, 2009

5.2 Gpc/h

DEUS FULL UNIVERSE RUN (2012)20 kpc/h 21 Gpc/hΛCDM

Ratra‐Peebles

Observable Horizon!Milky Way

Ratra PeeblesW’CDM

Jean‐Michel ALIMI

Dark Energy Universe Simulation Series: Temporal Evolution

CDM SUCDM RPCDM

- z=9

CDM SUCDM RPCDM

- z=6.5

- z=1

- z=0.25

- z=0.

Jean‐Michel ALIMI

Dark Energy Universe Simulation Series: Final Structuration z=0

C CCDM RPCDM

L = 162 h-1Mpc

L = 40 h-1Mpc

Jean‐Michel ALIMI


CDM RPCDM

L = 20 h-1Mpc

L = 10 h-1Mpc

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ΛCDMSugraRatra‐PeeblesPeebles

Degenerate DE models at homogeneous and linear level can leave distinctive features on the non‐linear scales !

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More than 50 000 000 halos

Dark Energy Universe Simulation Series: Very Large Halos Catalog.

More than 50 000 000 halos: Masses from 2.5 1010 h‐1 M⦿ to 1016 h‐1 M⦿Up to 500 000 halos per snapshotUp to 3 000 000 particles per halo

z=0

ΛCDMSugraR t P bl 1

z=2.3

Ratra‐Peebles z=1

Halo Mass Function (10243 FOF with b 0 2)

Jean‐Michel ALIMI

Halo Mass Function (10243, FOF with b=0.2)

Dark Energy Universe Simulation: Science Challenges

How DEUS can be useful for Observational Projects ?

Which Challenges from Observational Point of View ?

How DEUS can we help to better understandHow DEUS can we help to better understand the LSS formation process in presence of Dark Energy ?

Which Challenges from Theoretical Point of View

Jean‐Michel ALIMI

Dark Energy Universe Simulations Series: lightcones in redshift space.

All l h i l i b ild h• All along the simulation we build the matter distribution in redshift space.

• Particle and Halos are now as we observe them not as they are at a given time.

• Full sky lightcones from z=0 to z≈1.5, narrow li ht f 0 t 6 il bllightcones from z=0 to z=6 are available.

• The distortion in redshift space is characteristic of the cosmology.

• From lightcone we could directly compared to present and futur cosmologycal surveys

Jean‐Michel ALIMI

Dark Energy Universe Simulations Series: lightcones in redshift space.Particles and Halos as we see them (not as they are at a given instant)

Numerous DEUS Challenges: From Observational Point of View.Numerous DEUS Challenges: From Observational Point of View.DEUS provides useful results for the present and future cosmological surveys.

Jean‐Michel ALIMI

Dark Energy Universe Simulations Series: lightcones.Numerous DEUS Challenges: From Observational Point of View.Numerous DEUS Challenges: From Observational Point of View.

DEUS provides useful results for the present and future cosmological surveys.

Jean‐Michel ALIMI

Dark Energy Universe Simulation Series: Power Spectrum

Numerous DEUS Challenges: From Theoretical Point of View (1)

z=0z=0

z=0z=1


z=1z=2.3

z 0

z=1z=2.3

z=2.3

L=2592 hL=2592 h--11MpcMpc L=648 hL=648 h--11MpcMpc L=162 hL=162 h--11MpcMpc

Power Spectrum / Deep redshftDeep redshft

Survey

Jean‐Michel ALIMI

DEUS: Imprints of Dark Energy on the non‐linear matter power spectrum

CDMCDM

RPRP

SugraSugra

CDMCDM

SugraSugra

RPRPRPRP

Evolution of the non‐linear power spectrum in quintessence

Ratio of the non‐linear power spectrum relative to linear prediction

f h diff l i

DEUS Consortium MNRAS 401 775 (2010)

cosmologies relative to the CDM case

for the different cosmologies as a measurement of the evolution of non‐linearity in the gravitational

collapse.

Jean‐Michel ALIMI

DEUS Consortium, MNRAS 401, 775 (2010).

DEUS: Imprints of Dark Energy on the non‐linear matter power spectrum

RatraRatra‐‐PeeblesPeeblesRatraRatra‐‐PeeblesPeebles

CDMCDM

SugraSugraSugraSugra

CDMCDM

Ratio of the nonRatio of the non‐‐linear power spectrum linear power spectrum normalized to the Smith et al (2003) fitnormalized to the Smith et al (2003) fit

Linear growth factor relative to the Linear growth factor relative to the CDM CDM WMAP5 one.WMAP5 one.

Non‐linearities are different for each models. The deviations at high k of the power spectrum are correlated with the linear growth history

DEUS Consortium, MNRAS 401, 775 (2010).

Jean‐Michel ALIMI

spectrum are correlated with the linear growth history

DEUS : Anomalous cosmic flow, a challenge for CDMNumerous DEUS Challenges: From Theoretical Point of View (2)

Abnormal Observational signal on Bulk Flow in velocity surveys (Watkins et al 2008, Feldman et al 2008, Lavaux et al 2009 (2MRS survey (redshift survey)…)

High deviation from linear predictions


High deviation from linear predictions.

Mean of the (peculiar) velocity fields present in a sphere of radius R centered on the Milky Way

Is this signal a cosmological one or is it an unlikely event?Is ()Cold DarkMatter Scenario ruled out?

Jean‐Michel ALIMI

Is ()Cold Dark Matter Scenario ruled out?

For gaussian initial conditions is such a V possible ?

DEUS : Anomalous cosmic flow, a challenge for CDMFor gaussian initial conditions, is such a Vbulk possible ?

Is it not a rare event ?As a first approximation, we can characterize the Watkins curve by two data points:depletion at 16 h‐1 Mpc and bump at 53 h‐1 Mpc. We then compute the Probability toget such a event from initial condition statistics.

Strong correlation between scales R16 and R53, M is the correlation matrix containing non‐diagonal terms, (tail of a 2D maxwellian).

P ≈ 1.4 %Watkins Vbulk could be a rare event realization in CDM!

Jean‐Michel ALIMI

Watkins Vbulk could be a rare event realization in CDM!

DEUS : Anomalous cosmic flow, a challenge for CDM

DEUS: CDM‐WMAP5 , 10243 particles, 648 h‐1 Mpc.

F 20 000 dFrom 20.000 random centers (environments)

Using Watkins observational data points: We isolate an observational‐like sample at 95% (χ2 analysis done on all observational 10 data points) 255 t f 20 000

Rare events (P ≈1.3%) in (very good) agreement with the previous estimation

: 255 out of 20.000.

Jean‐Michel ALIMI

in (very good) agreement with the previous estimation.

Watkins Bulk Flows Numerical Catalog (255)


Watkins Bulk Flows Numerical Catalog (255)

(R) in good agreement with linear predictions

r V Bulk (R)

seems to diverge from the linear prediction.

B r

V (R) i di i l But V Bulk (R) is directional

To solve this Contradiction.What is the Dynamical Origin of Bulk Flow ?

Jean‐Michel ALIMI

What is the Dynamical Origin of Bulk Flow ?


W h W ki Wh i h D i l O i i f B lk Fl ?We suppose such a Watkins event, What is the Dynamical Origin of Bulk Flow ?Directionality suggests an asymmetry in matter distribution.

We then qualitatively study the direction of the bulk flow versus the direction of the center of mass; For a Watkins Numerical event Mollweide projection (53 h‐1 Mpc)For a Watkins Numerical event, Mollweide projection (53 h Mpc)

1Bulk direction at 53 h‐1 Mpc

CM direction at 53 h‐1 Mpc

Clearly distinct direction

Jean‐Michel ALIMI

Clearly distinct direction


W h W ki Wh i h D i l O i i f B lk Fl ?We suppose such a Watkins event, What is the Dynamical Origin of Bulk Flow ?Directionality suggests an asymmetry in matter distribution.

We then qualitatively study the direction of the bulk flow versus the direction of the center of mass; For a Watkins Numerical event Mollweide projection at larger scale (85 h‐1 Mpc)For a Watkins Numerical event, Mollweide projection at larger scale (85 h Mpc)

1Bulk direction at 53 h‐1 Mpc

CM direction at 85 h‐1 Mpc

Clearly similar direction

Jean‐Michel ALIMI

Clearly similar direction


Wh t i th D i l O i i f B lk Fl ?

r C (R) Which scales shows an alignment between the

direction of asymmetry in a shell and the direction of

What is the Dynamical Origin of Bulk Flow ?

We compare the mean for the complete Watkins bulk flow numerical catalog y y

the Bulk Flow at 53 h‐1 Mpc ?p g

and for the Linear bulk flow numerical catalog:

r V Bulk (53h1Mpc) . (

r C (R dR)

r C (R))~ 85 h‐1 Mpc (bump)

~ 55 h‐1 Mpc (depletion) Alignment scale at ~ 85 h-1 Mpc

Jean‐Michel ALIMI

(85 = 53 + 32)


Wh t i th D i l O i i f B lk Fl ?What is the Dynamical Origin of Bulk Flow ?

For all events from the complete Watkins bulk flow numerical catalog, we compute the scalar product of the bulk flow at radius R with the direction of the asymmetry in spheres of radius R+32

r V Bulk (R) .

r C (R 32h1Mpc)

Alignment scale from 53 h‐1 Mpc

Jean‐Michel ALIMI

Alignment scale from 53 h 1 Mpc.


r V Bulk(R,z) = H(z) f(z) 2

P (k,z) W2(kR)dk

We confirm that Vbulk is a linear quantity even for such a rare event. Its evolution satisfies the linear evolution, idem for the asymmetry factor

P (k ) D+(z)

2

P (k 0)

V Bulk(R,z) H(z) f(z) P (k,z) W (kR)dk

r V Bulk(R,0) = H(0) f(0) 2

P (k,0) W2(kR)dk

P (k,z) = +( )

D+(0)

P (k,0)

r V bulk (R,z) =

H(z) f (z)D(z)

H(0) f (0)D (0)

r V bulk (R,0)

bulk ( )

H(0) f (0)D(0)

bulk ( )

r V bulk (R,z)

r C (R,z)

Jean‐Michel ALIMI


Where is the cosmology ?

Likelihood analysis on the Vbulk data for the Watkins Bulk Flow catalog (R≤53 h-1 Mpc and R≤130 h‐1 Mpc)( p )

Wrong cosmological parameters (R ≤ 53 h‐1 Mpc). Correct cosmological parameters (R=130 h‐1 Mpc).

Jean‐Michel ALIMI

Conclusion

k lk l b b• Watkins Bulk Flow observations can be seen as a rare event.

• Dynamical origin of such a high Bulk Flow comes from an asymmetry of the matter at higher scalesasymmetry of the matter at higher scales.

Th i t di ti ith li di ti i (CDM• There is no contradiction with linear prediction in (CDM

• How the mean value of the linear prediction is recovered at higher scales should (could) be a “signature” of the Cosmology

Jean‐Michel ALIMI

Cosmology….

Dark Energy Universe Simulation: Open DataFor the first time all numerical data (fields halos lightcones) fromFor the first time, all numerical data (fields, halos, lightcones) from

A large set of high resolution N‐body simulations for various cosmological models with Dark Energy

are available on free public database.

ROXXOR.OBSPM.FR/DEUVO‐UI

Thank you for your attention

Jean‐Michel ALIMI

Thank you for your attention...

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