is dark energy phantom-like? what do the recent...
TRANSCRIPT
Is Dark Energy Phantom-like?What Do the Recent Observations Tell Us?
Supratik Pal
Indian Statistical Institute Kolkata
September 5, 2015
Based on
• Hazra, Majumdar, Pal, Panda, Sen: PRD (2015) [1310.6161]• Adak, Majumdar, Pal: MNRAS (2014)
Cited in Thirty Meter Telescope’s Science Report 2015 togetherwith Planck 2015 paper [1505.01195]
Based on
• Hazra, Majumdar, Pal, Panda, Sen: PRD (2015) [1310.6161]• Adak, Majumdar, Pal: MNRAS (2014)
Cited in Thirty Meter Telescope’s Science Report 2015 togetherwith Planck 2015 paper [1505.01195]
Questions
Is Dark Energy Equation of State unique?
Is it observation dependent?
Is it parametrization (theoretical prior) dependent?
What can be the possible wayout?
Dark Energy a la observations
Supernova Type Ia Data
Probe Luminosity distance: DL(z) = H0dL(z) via distance modulus
µ(z) = 5 log10(DL(z)) + µ0
χ2SN(w0
X ,Ω0m,H0) =
∑i
[µobs(zi )−µ(zi ;w
0X ,Ω
0m,H0)
σi
]2
Marginalizing over the nuisance parameter µ0,
χ2SN(w0
X ,Ω0m) = A− B2/C
A =∑
i
[µobs(zi )−µ(zi ;w
0X ,Ω
0m,µ0=0)
σi
]2
B =∑
i
[µobs(zi )−µ(zi ;w
0X ,Ω
0m,µ0=0)
σi
];C =
∑i
1σ2i
Union 2.1 compilation of 580 Supernovae at z = 0.015− 1.4,considered as standard candles
Baryon Acoustic Oscillation (BAO) data
Used to measure H(z) and angular diameter distance DA(z) via acombination
DV (z) =[(1 + z)2D2
A(z) czH(z)
]1/3
Confront models via a distance ratio
dz =rs(zdrag)DV (z)
rs(zdrag) = comoving sound horizon at a redshift wherebaryon-drag optical depth is unity
Give 6 data points:
WiggleZ : z = 0.44, 0.6, 0.73
SDSS DR7 : z = 0.35
SDSS DR9 : z = 0.57
6DF : z = 0.106
Hence calculate χ2BAO
Hubble Space Telescope Data (HST)
Use nearby Type-Ia Supernova data with Cepheid calibrations toconstrain the value of H0 directly.Combine and calculate χ2 for the analysis of HST data
χ2HST(w0
X ,Ω0m,H0) =
∑i
[Hobs(zi )−H(zi ;w
0X ,Ω
0m,H0)
σi
]2
Two methods of analysis
Riess et. al. (2011)
Efstathiou (2014)
Cosmic Microwave Background (CMB) data
Reflection on Dark Energy
CMB shift parameter (position of peaks)
Integrated Sachs-Wolfe effect (low-`)
Shift ParameterDE ⇔ Shift in position of peaks by
√ΩmD
D= Angular diameter distance (to LSS) ⇒ Shift Parameter
R =√
Ωmh2
|Ωk |h2χ(y)
χ(y) = sin y(k < 0) ; = y(k = 0) ; = sinh y(k > 0)
y =√|Ωk |
∫ zdeco
dz√Ωm(1+z)3+Ωk (1+z)2+ΩX (1+z)3(1+ωX )
χ2CMB(ωX ,Ωm,H0) =
[R(zdec,ωX ,Ωm,H0)−R
σR
]2
Cosmic Microwave Background (CMB) data
Reflection on Dark Energy
CMB shift parameter (position of peaks)
Integrated Sachs-Wolfe effect (low-`)
Shift ParameterDE ⇔ Shift in position of peaks by
√ΩmD
D= Angular diameter distance (to LSS) ⇒ Shift Parameter
R =√
Ωmh2
|Ωk |h2χ(y)
χ(y) = sin y(k < 0) ; = y(k = 0) ; = sinh y(k > 0)
y =√|Ωk |
∫ zdeco
dz√Ωm(1+z)3+Ωk (1+z)2+ΩX (1+z)3(1+ωX )
χ2CMB(ωX ,Ωm,H0) =
[R(zdec,ωX ,Ωm,H0)−R
σR
]2
Integrated Sachs-Wolfe EffectSome CMB anisotropies may be induced by passing through a timevarying gravitational potential
linear regime: integrated Sachs-Wolfe effect
non-linear regime: Rees-Sciama effect
Poisson equation : ∇2Φ = 4πGa2ρδ
Φ→ constant during matter domination→ time-varying when dark energy comes to dominate
(at large scales l ≤ 20)
Cl =∫
dkk PR(k)T 2
l (k)
T ISWl (k) = 2
∫dη exp−τ dΦ
dη jl(k(η − η0)
But cosmic variance !
Dark Energy Perturbations
Can be important at horizon scales.Need to cross-correlate large scale CMB with large scale structures.
But cosmic variance!
Incorporated through sound speed squared c2s .
For canonical scalar, c2s = 1
Dark energy parametrizations
Parametrize the Hubble parameter
Parametrize the Equation of State (EOS) of Dark Energy
Parametrization of Hubble parameter
r(x) = H2(x)H2
0= Ω0
mx3 + A0 + A1x + A2x
2
with x = 1 + z ; Ω0m + A0 + A1 + A2 = 1 ; ρ0
c = 3H20
ρ = ρ0c
(A0 + A1x + A2x
2)
For A0 6= 0,A1 = 0 = A2 =⇒ ΛCDM
Either A1 6= 0 or A2 6= 0 =⇒ Dynamical dark energy
Dark energy parametrizations
Parametrize the Hubble parameter
Parametrize the Equation of State (EOS) of Dark Energy
Parametrization of Hubble parameter
r(x) = H2(x)H2
0= Ω0
mx3 + A0 + A1x + A2x
2
with x = 1 + z ; Ω0m + A0 + A1 + A2 = 1 ; ρ0
c = 3H20
ρ = ρ0c
(A0 + A1x + A2x
2)
For A0 6= 0,A1 = 0 = A2 =⇒ ΛCDM
Either A1 6= 0 or A2 6= 0 =⇒ Dynamical dark energy
Parametrization of EOS
• CPL Parametrization
Fits a wide range of scalar field dark energy models including thesupergravity-inspired SUGRA dark energy models.
w(a) = w0 + wa(1− a)
= w0 + waz
1 + z
ρDE ∝ a−3(1+w0+wa)e−3wa(1−a)
Two parameter description: w0 = EOS at present , wa = itsvariation w.r.t. scale factor (or redshift).
For w0 ≥ −1,wa > 0 : dark energy is non-phantomthroughout
Otherwise, may show phantom behavior at some point
• SS Parametrization
Useful for slow-roll ‘thawing’ class of scalar field models having acanonical kinetic energy term.Motivation : to look for a unique dark energy evolution for scalarfield models that are constrained to evolve close to Λ.
w(a) = (1 + w0)×[√1 + (Ω−1
DE − 1)a−3 − (Ω−1DE − 1)a−3 tanh−1 1√
1+(Ω−1DE−1)a−3
]2
×[1√
ΩDE−(
1ΩDE− 1)
tanh−1√
ΩDE
]−2− 1
One model parameter: w0 = EOS at present
Rest is taken care of by the general cosmological parameterΩDE = dark energy density today.
• GCG Parametrization
p = − cρα
w(a) = − AA+(1−A)a−3(1+α) ; A = c
ρ1+αGCG
Two model parameters e.g A and α, with w(0) = −AFor (1 + α) > 0, w(a) behaves like a dust in the past andevolves towards negative values and becomes w = −1 in theasymptotic future. =⇒ ‘tracker/freezer’ behavior
For (1 + α) < 0, w(a) is frozen to w = −1 in the past and itslowly evolves towards higher values and eventually behaveslike a dust in the future. =⇒ ‘thawing’ behavior
Restricted to 0 < A < 1 only since for A > 1 singularityappears at finite past =⇒ non-phantom only
Dark energy from different datasets
Used all three parametrizations =⇒ Analysis is robust
Data ΛCDM CPL SS GCG
Planck (low-` + high-`) 7789.0 7787.4 7788.1 7789.0
WMAP-9 low-` polarization 2014.4 2014.436 2014.455 2014.383
BAO : SDSS DR7 0.410 0.073 0.265 0.451
BAO : SDSS DR9 0.826 0.793 0.677 0.777
BAO : 6DF 0.058 0.382 0.210 0.052
BAO : WiggleZ 0.020 0.069 0.033 0.019
SN : Union 2.1 545.127 546.1 545.675 545.131
HST 5.090 2.088 2.997 5.189
Total 10355.0 10351.4 10352.4 10355.0
Best fit χ2eff obtained in different model upon comparing against
CMB + non-CMB datasets using the Powell’s BOBYQA methodof iterative minimization.
Likelihood functions for CPL parametrization
0
0.5
1
-2 -1.5 -1 -0.5 0 0.5 1
Lik
elih
oo
d
w0
CPL model
Planck+WP
non-CMB
Planck+WP+non-CMB
0
0.5
1
-3 -2 -1 0 1 2 3
Lik
elih
oo
dwa
CPL model
Planck+WP
non-CMB
Planck+WP+non-CMB
Likelihood functions for different parameters of EOS
0
0.5
1
-2 -1.5 -1 -0.5 0 0.5 1
Lik
elihood
w0
CPL model
Planck+WP
non-CMB
Planck+WP+non-CMB
0
0.5
1
-3 -2 -1 0 1 2 3
Lik
elihood
wa
CPL model
Planck+WP
non-CMB
Planck+WP+non-CMB
0
0.5
1
-2 -1.5 -1 -0.5 0
Lik
elihood
w0
SS model
Planck+WP
non-CMB
Planck+WP+non-CMB
0
0.5
1
-1 -0.9 -0.8 -0.7 -0.6
Lik
elih
ood
-A (w0)
GCG model
Planck+WP
non-CMB
Planck+WP+non-CMB
0
0.5
1
-3 -2 -1 0 1 2 3
Lik
elihood
α
GCG model
Planck+WP
non-CMB
Planck+WP+non-CMB
Analysis: value of H0
CPL model
0.25 0.27 0.29 0.31 0.33Ωm
66
68
70
72
74
76
H0
GCG model
0.26 0.28 0.3 0.32Ωm
66
68
70
72
74
76
H0
SS model
0.26 0.28 0.3 0.32Ωm
66
68
70
72
74
H0
CPL model
-1.6 -1.4 -1.2 -1 -0.8 -0.6w0
66
68
70
72
74
76
H0
GCG model
-1 -0.95 -0.9 -0.85 -0.8 -0.75 -0.7w0
66
68
70
72
74
76
H0
SS model
-1.4 -1.3 -1.2 -1.1 -1 -0.9w0
66
68
70
72
74
H0
If phantom is forbidden by theoretical prior (GCG):
The parameters stay close to the values obtained in ΛCDMmodel analysis.H0 is not that degenerate with dark energy equation of statefor CMB.
If phantom is not forbidden by theoretical prior (CPL+SS):
Better fit to the CMB data comes with a large value of H0
⇒ agrees better with the HST data (better total χ2)But background cosmological parameter space (e.g., Ωm −H0)is dragged s.t. best-fit base model and that from Planckbecomes 2σ away.H0 becomes highly degenerate with dark energy EOS for CMBonly measurements.
If phantom is forbidden by theoretical prior (GCG):
The parameters stay close to the values obtained in ΛCDMmodel analysis.H0 is not that degenerate with dark energy equation of statefor CMB.
If phantom is not forbidden by theoretical prior (CPL+SS):
Better fit to the CMB data comes with a large value of H0
⇒ agrees better with the HST data (better total χ2)But background cosmological parameter space (e.g., Ωm −H0)is dragged s.t. best-fit base model and that from Planckbecomes 2σ away.H0 becomes highly degenerate with dark energy EOS for CMBonly measurements.
Analysis: Equation of State
CPL model
-1.6 -1.4 -1.2 -1 -0.8 -0.6w0
-3
-2
-1
0
1
wa
Always non-phantom
GCG model
-1 -0.9 -0.8 -0.7-A (w0)
-3
-2
-1
0
α
Freezing/tracking
Thawing
0.0 0.5 1.0 1.5 2.0-1.6
-1.4
-1.2
-1.0
-0.8
z
w
0.0 0.5 1.0 1.5 2.0-1.6
-1.4
-1.2
-1.0
-0.8
z
w
0.0 0.5 1.0 1.5 2.0-1.00
-0.98
-0.96
-0.94
-0.92
-0.90
z
w
Top: CPL, Bottom left: SS, Bottom right: GCG
If phantom is forbidden by theoretical prior (GCG):
Show consistency between CMB and non-CMB dataBut they have marginally worse likelihood than otherparametrizations.CMB and non-CMB observations are separately sensitive tothe two model parameters but the joint constraint is consistentwith w = −1.
If phantom is not forbidden by theoretical prior (CPL+SS):
CMB data: the non-phantom equation of states stays at theedge of 2σ region.Non-CMB data: non-phantom behavior favored for everyparametrization considered.
If phantom is forbidden by theoretical prior (GCG):
Show consistency between CMB and non-CMB dataBut they have marginally worse likelihood than otherparametrizations.CMB and non-CMB observations are separately sensitive tothe two model parameters but the joint constraint is consistentwith w = −1.
If phantom is not forbidden by theoretical prior (CPL+SS):
CMB data: the non-phantom equation of states stays at theedge of 2σ region.Non-CMB data: non-phantom behavior favored for everyparametrization considered.
Combined CMB + non-CMB data
Mean w and error bar depends on the parametrization.
SS and GCG parametrization: the nature of dark energy isbest constrained at high redshifts
CPL parametrization: the best constraints come in theredshift range of ≈ 0.2− 0.3
Just as aside...
Similar results by Novosyadlyj et.al. (JCAP): for datasetPlanck+HST+BAO+SNLS3 ΛCDM is outside 2σ confidenceregime, for dataset WMAP-9+HST+BAO+SNLS3 ΛCDM is1σ away from best fit.
PAN-STARRS1 shows tension with ΛCDM at 2.4σ with aconstant EOS (Rest et.al., 1310.3828)
So, what next?
Constraints on w and hence the nature of dark energy that weinfer from cosmological observation depends crucially on thechoice of the underlying parametrization of the EOS.
Can the apparent tension between CMB and non-CMB databe attributed to unknown systematics?
Unlikely!
Can it be due to different analysis of HST data (Riess vsEfsthathiou)?
Maybe
Can it be due to lack of a better theory/parametrization ofthe dark energy equation of state?
Most likely yes
Can a non-parametric reconstruction of w for the totaldataset help to infer about the correct nature of dark energy(or, Λ) without any priors on the form of w?
So, what next?
Constraints on w and hence the nature of dark energy that weinfer from cosmological observation depends crucially on thechoice of the underlying parametrization of the EOS.
Can the apparent tension between CMB and non-CMB databe attributed to unknown systematics?
Unlikely!
Can it be due to different analysis of HST data (Riess vsEfsthathiou)?
Maybe
Can it be due to lack of a better theory/parametrization ofthe dark energy equation of state?
Most likely yes
Can a non-parametric reconstruction of w for the totaldataset help to infer about the correct nature of dark energy(or, Λ) without any priors on the form of w?
So, what next?
Constraints on w and hence the nature of dark energy that weinfer from cosmological observation depends crucially on thechoice of the underlying parametrization of the EOS.
Can the apparent tension between CMB and non-CMB databe attributed to unknown systematics?
Unlikely!
Can it be due to different analysis of HST data (Riess vsEfsthathiou)?
Maybe
Can it be due to lack of a better theory/parametrization ofthe dark energy equation of state?
Most likely yes
Can a non-parametric reconstruction of w for the totaldataset help to infer about the correct nature of dark energy(or, Λ) without any priors on the form of w?
So, what next?
Constraints on w and hence the nature of dark energy that weinfer from cosmological observation depends crucially on thechoice of the underlying parametrization of the EOS.
Can the apparent tension between CMB and non-CMB databe attributed to unknown systematics?
Unlikely!
Can it be due to different analysis of HST data (Riess vsEfsthathiou)?
Maybe
Can it be due to lack of a better theory/parametrization ofthe dark energy equation of state?
Most likely yes
Can a non-parametric reconstruction of w for the totaldataset help to infer about the correct nature of dark energy(or, Λ) without any priors on the form of w?
So, what next?
Constraints on w and hence the nature of dark energy that weinfer from cosmological observation depends crucially on thechoice of the underlying parametrization of the EOS.
Can the apparent tension between CMB and non-CMB databe attributed to unknown systematics?
Unlikely!
Can it be due to different analysis of HST data (Riess vsEfsthathiou)?
Maybe
Can it be due to lack of a better theory/parametrization ofthe dark energy equation of state?
Most likely yes
Can a non-parametric reconstruction of w for the totaldataset help to infer about the correct nature of dark energy(or, Λ) without any priors on the form of w?