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An Approach to Testing
Dark Energy by Observations
Collaborators : Chien-Wen Chen 陳建文 @ Phys, NTU Pisin Chen 陳丕燊 @ LeCosPA, NTU
Je-An Gu 顧哲安臺灣大學梁次震宇宙學與粒子天文物理學研究中心
Leung Center for Cosmology and Particle Astrophysics (LeCosPA), NTU
2009/11/20 @ CosPA 2009, Melbourne
An Approach to Testing
Dark Energy by Observations
Collaborators : Chien-Wen Chen 陳建文 @ Phys, NTU Pisin Chen 陳丕燊 @ LeCosPA, NTU
Je-An Gu 顧哲安臺灣大學梁次震宇宙學與粒子天文物理學研究中心
Leung Center for Cosmology and Particle Astrophysics (LeCosPA), NTU
2009/11/20 @ CosPA 2009, Melbourne
References
Je-An Gu, Chien-Wen Chen, and Pisin Chen, “A new approach to testing dark energy models by observations,”
New Journal of Physics 11 (2009) 073029 [arXiv:0803.4504].
Chien-Wen Chen, Je-An Gu, and Pisin Chen, “Consistency test of dark energy models,”
Modern Physics Letters A 24 (2009) 1649 [arXiv:0903.2423].
Concordance: = 0.73 , M = 0.27
Accelerating Expansion
(homogeneous & isotropic)
Based on FLRW Cosmology
Dark Energy
Observations (which are driving Modern Cosmology)
(Non-FLRW)
Models: Dark Geometry vs. Dark Energy
Einstein Equations
Geometry Matter/Energy
Dark Geometry
↑Dark Matter / Energy
↑
Gμν = 8πGNTμν
• Modification of Gravity
• Averaging Einstein Equations
• Extra Dimensions
for an inhomogeneous universe
(from vacuum energy)
• Quintessence/Phantom
(based on FLRW)
M1 (O)
M2 (O)
M3 (X)
M4 (X)
M5 (O)
M6 (O)
:
:
:
ObservationsData
Data Analysis
ModelsTheories
mapping out the evolution history
(e.g. SNe Ia , BAO) (e.g. 2 fitting)
Data
:
:
:
Reality : Many models survive
An Approach to Testing
Dark Energy Models
via Characteristic Q(z)
Gu, C.-W. Chen and P. Chen, New J. Phys. [arXiv:0803.4504] C.-W. Chen, Gu and P. Chen, Mod. Phys. Lett. A [arXiv:0903.2423]
Characteristic Q(z)
1. Q(z) is time-varying (i.e. dependent on z) in general.
2. Q(z) is constant within the model M (under consideration).
3. Q(z) plays the role of a key parameter within Model M.
4. Q(z) is a functional of the parametrized physical quantity P(z).
5. Q(z) can be reconstructed from data via the constraint on P(z).
6. dQ(z)/dz can also be reconstructed from data.
7. The (in)compatibility of the observational constraint of M dQ(z)/dz and the theoretical prediction of dQ(z)/dz : “0”tells the (in)consistency between data and Model M.
For each model, introduce a characteristic Q(z) with the following features:
Gu, CW Chen & P ChenarXiv:0803.4504
E.g., CDM
DE(z): energy density
wDE(z) = w0 + waz/(1+z)
Along a similar line of thought, focusing on CDM:
Sahni, Shafieloo and Starobinsky, PRD [0807.3548]:
Zunckel and Clarkson 2008, PRL101 [0807.4304]:
111)( 320
2 zHzHzOm
cDE zzQ 1)(
Q1(z)
Q2(z)
Q3(z)
:
Qi(z)
:
:
:
M1
M2
M3
:
Mi
:
:
:
Model
(parametrization)
DataP(z)
Constraints on
Parameters
Test the Consistency between Models and DataGu, CW Chen and P Chen, 2008
Characteristic Q
Qi [P(z),z]
in
Measure of Consistency M
M1
M2
M3
:
Mi
:
:
:
Model
(parametrization)
DataP(z)
Constraints on
Parameters
Test the Consistency between Models and DataGu, CW Chen and P Chen, 2008
Mi dQi (z)/dz
:
:
:
:
reconstr
uct
observationalconstraint
:
:
:
:theoretical
prediction: 0
consistentinconsistent
Q1(z)
Q2(z)
Q3(z)
:
Qi(z)
:
:
:
M1(z)
M2(z)
M3(z)
:
Mi(z)
:
:
:
in
Q1(z)
Q2(z)
Q3(z)
:
Qi(z)
:
:
:
M1(z)
M2(z)
M3(z)
:
Mi(z)
:
:
:
Measure of Consistency M
M1
M2
M3
:
Mi
:
:
:
Model
(parametrization)
DataP(z)
Constraints on
Parameters
Test the Consistency between Models and DataGu, CW Chen and P Chen, 2008
Mi dQi (z)/dz
:
:
:
:
reconstr
uct
observationalconstraint
:
:
:
:theoretical
prediction: 0
consistentinconsistent
SN Ia (Constitution)CMB (WMAP 5)BAO (SDSS,2dFGRS)
in
parameters:{m,w0,wa}
z
zwwzw a
1)( 0DE
Linder, 2003
Chevallier&Polarski, 2001
Q1(z)
Q2(z)
Q3(z)
:
Qi(z)
:
:
:
M1(z)
M2(z)
M3(z)
M4(z)
M5(z)
:
:
:
Measure of Consistency M
Qexp
Qpower
Qinv-exp
Chaplygin
:
:
:
Model
(parametrization)
DataP(z)
Constraints on
Parameters
Test the Consistency between Models and Data
Mi dQi (z)/dz
:
:
:
reconstr
uct
observationalconstraint
:
:
:theoretical
prediction: 0
consistentinconsistent
SN Ia (Constitution)CMB (WMAP 5)BAO (SDSS,2dFGRS)
parameters:{m,w0,wa}
in
z
zwwzw a
1)( 0DE
Linder, 2003
Chevallier&Polarski, 2001
CW Chen, Gu and P Chen, 2009
exp-inv:Quint. in 14
2
22
2
23
exp-inv Md
dV
Vd
Vd
d
dV
VzQ
CDM in DE zzQ
exp:Quint. in 1
1
exp Mzd
dVzVzQ
law-power:Quint. in 1
1
2
22
power nzd
Vdz
d
dVzVzQ
Chaplygin in 1
1
DEDEChaplygin
zdz
dz
dz
dw
zw
zzQ
Characteristics Q(z) of 5 Models
CDM : = constant
Quintessence, exponential: V() = V1exp[/M1]
Quintessence, power-law: V() = m4nn
Quintessence, inverse-exponential: V() = V2exp[M2/]
generalized Chaplygin gas: pDE(z) = A/DE(z) , A>0, 1
CW Chen, Gu and P Chen, 2009Gu, CW Chen and P Chen, 2008
Testing DE Models: Results
Q1(z)
Q2(z)
Q3(z)
:
Qi(z)
:
:
:
M1(z)
M2(z)
M3(z)
M4(z)
M5(z)
:
:
:
Measure of Consistency M
Qexp
Qpower
Qinv-exp
Chaplygin
:
:
:
Model
(parametrization)
DataP(z)
Constraints on
Parameters
Test the Consistency between Models and Data
Mi dQi (z)/dz
:
:
:
reconstr
uct
observationalconstraint
:
:
:theoretical
prediction: 0
consistentinconsistent
SN Ia (Constitution)CMB (WMAP 5)BAO (SDSS,2dFGRS) z
zwwzw a
1)( 0DE
Linder, PRL, 2003
parameters:{m,w0,wa}
in
Gu, CW Chen and P Chen, 2008 CW Chen, Gu and P Chen, 2009
Q1(z)
Q2(z)
Q3(z)
:
Qi(z)
:
:
:
M1(z)
M2(z)
M3(z)
M4(z)
M5(z)
:
:
:
Measure of Consistency M
Qexp
Qpower
Qinv-exp
Chaplygin
:
:
:
Model
(parametrization)
DataP(z)
Constraints on
Parameters
Test the Consistency between Models and Data
Mi dQi (z)/dz
:
:
:
reconstr
uct
observationalconstraint
:
:
:theoretical
prediction: 0
consistentinconsistent
SN Ia (Constitution)CMB (WMAP 5)BAO (SDSS,2dFGRS)
CW Chen, Gu and P Chen, 2009
in
parameters:{m,w0,wa}
z
zwwzw a
1)( 0DE
Linder, 2003
Chevallier&Polarski, 2001
CDM: measure of consistency M dQ(z)/dz
CDM : = constant CDM in DE zzQ
95.4% C.L.
68.3% C.L.
consistent
CW Chen, Gu and P Chen, 2009
dz
zdQz i
i M
Quintessence: Exponential potential
exp:Quint. in 1
1
exp Mzd
dVzVzQ
Quintessence, exponential: V() = V1exp[/M1]
95.4% C.L.
68.3% C.L.
inconsistent
CW Chen, Gu and P Chen, 2009
dz
zdQz i
i M
Quintessence: Power-law potential
law-power:Quint. in 1
1
2
22
power nzd
Vdz
d
dVzVzQ
Quintessence, power-law: V() = m4nn
95.4% C.L.
68.3% C.L.
consistent
CW Chen, Gu and P Chen, 2009
dz
zdQz i
i M
Quintessence: Inverse-exponential potential
exp-inv:Quint. in 14
2
22
2
23
exp-inv Md
dV
Vd
Vd
d
dV
VzQ
Quintessence, inverse-exponential: V() = V2exp[M2/]
95.4% C.L.
68.3% C.L.
consistent
CW Chen, Gu and P Chen, 2009
dz
zdQz i
i M
Generalized Chaplygin Gas
Chaplygin in 1
1
DEDEChaplygin
zdz
dz
dz
dw
zw
zzQ
generalized Chaplygin gas: pDE(z) = A/DE(z) , A>0, 1
95.4% C.L.
68.3% C.L.
consistent
CW Chen, Gu and P Chen, 2009
dz
zdQz i
i M
Measure of Consistency for 5 DE ModelsCW Chen, Gu and P Chen, 2009
dz
zdQz i
i M
Discriminative Power
between Dark Energy Models
exp:Quint. in
1
1
exp
M
zd
dVzVzQ
law-power:Quint. in
1
1
2
22
power
n
zd
Vdz
d
dVzVzQ
Distinguish …
Quintessence, exponential:
V() = V1exp[/M1] Quintessence, power-law: V() = m4nn
Gu, CW Chen and P Chen, 2009
from
z
zwDE
1
2.08.0
M5
z
zwDE
1
2.005.1
M6
z
zwDE
1
5.06.0
M7
z
zwDE
1
0.105.1
M8
z
zwDE
1
5.01
M31 wwDE
M18.0DEw
M2
z
zwDE
1
5.11
M4
(8 models)
M1(z)
M2(z)
M3(z)
Qexp
Qpower
(parametrization)
DataP(z)
Constraints on
Parameters
Procedures
reconstruct2023 SNe (SNAP quality)CMB (WMAP5 quality)BAO (current quality)
Gu, CW Chen and P Chen, 2009
FiducialModels
M1,…,M8
simulation
in
observationalconstraint
theoreticalprediction: 0
indistinguishable
distinguishable
ModelMeasure of Consistency M
Mi dQi (z)/dzparameters:{m,w0,wa}
z
zwwzw a
1)( 0DE
Linder, 2003
Chevallier&Polarski, 2001
Distinguish from 8 models (M1–M8) Gu, CW Chen and P Chen, 2009
Exp. potential Power-law …
exp.
power-law
exp.
power-law
more slowly evolving wDE(z) faster evolving wDE(z)
O O O O
O O
O O
O O
O O
X X
X X
Summary
We proposed an approach to the testing of dark energy models
by observational results via a characteristic Q(z) for each model.
We performed the consistency test of 5 dark energy models: CDM, generalized Chaplygin gas, and 3 quintessence with exponential, power-law, and inverse-exponential potentials. The exponential potential is ruled out at 95.4% C.L. while the other 4 models are consistent with current data.
With the future observations and via our approach:
– Exponential potential: distinguishable from the 8 models (under consideration).
– Power-law potential: distinguishable from the models with faster evolving w(z) [M3,M4,M7,M8]; but NOT from those with more slowly evolving w(z) [M1,M2,M5,M6].
Summary and Discussions
The consistency test is to examine whether the condition necessary for a model is excluded by observations.
Our approach to the consistency test is simple and efficient because: For all models, Q(z) and dQ/dz are reconstructed from data via the observational constraints on a single parameter space that by choice can be easily accessed.
By our design of Q(z), the consistency test can be performed without the knowledge of the other parameters of the models.
Generally speaking, an approach invoking parametrization may be accompanied by a bias against certain models. This issue requires further investigation.
0
dz
zdQ
Summary and Discussions (cont.)
This approach can be applied to other DE models and other explanations of the cosmic acceleration.
The general principle of this approach may be applied to other cosmological models and even those in other fields beyond the scope of cosmology.
Summary and Discussions (cont.)
Thank you.
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