constraining the ultra-large scale structure of the...
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Constraining the Ultra-Large Scale Structure ofthe Universe Using Numerial Relativity
Jonathan Braden
University College London
GRG21, Columbia University, New York, July 12, 2016
w/ Hiranya Peiris, Matthew Johnson, and Anthony Aguirre
based on arXiv:1604.04001 and in progress
Ultra-Large Scale Structure
Local Remnants of Ultra-Large Scale Structure?
IStructure present at start of inflation
IConversion of structure during or after inflation
Ultra-Large Scale Structure
Local Remnants of Ultra-Large Scale Structure?
IStructure present at start of inflation
IConversion of structure during or after inflation
Ultra-Large Scale Structure
Local Remnants of Ultra-Large Scale Structure?
IStructure present at start of inflation
IConversion of structure during or after inflation
Modelling Initial Conditions
Monte Carlo Sampling: Planar Symmetry
ds2 = �d⌧2
+ a2k(x , ⌧)dx2 + a2?(x , ⌧)�dy2 + dz2
�
Inflaton on ak(⌧ = 0) = 1 = a?(⌧ = 0)
�(x) = ¯� + � ˆ�
¯� gives N e-folds 3H2
I
⌘ V (
¯�)
Field Fluctuations
� ˆ�(xi ) = A�
X
n=1
ˆGe iknxipP(kn) ˆG =
q�2 ln
ˆ�e2⇡i↵
P(k) = ⇥(kmax
� k) H�1
I
kmax
= 2⇡p3
Modelling Initial Conditions
Monte Carlo Sampling: Planar Symmetry
ds2 = �d⌧2
+ a2k(x , ⌧)dx2 + a2?(x , ⌧)�dy2 + dz2
�
Inflaton on ak(⌧ = 0) = 1 = a?(⌧ = 0)
�(x) = ¯� + � ˆ�
¯� gives N e-folds 3H2
I
⌘ V (
¯�)
Field Fluctuations
� ˆ�(xi ) = A�
X
n=1
ˆGe iknxipP(kn) ˆG =
q�2 ln
ˆ�e2⇡i↵
P(k) = ⇥(kmax
� k) H�1
I
kmax
= 2⇡p3
Model Choices
V (�) = V0
✓1 � e
�q
2
3↵�
MP
◆2
I ↵ ⌧ 1 ! small-field model
I ↵ = 1 ! Starobinski
I ↵ � 1 ! m2�2/2
Model Choices
V (�) = V0
✓1 � e
�q
2
3↵�
MP
◆2
0 1 2 3 4 5 6
�/MP
0.0
0.2
0.4
0.6
0.8
1.0
1.2
V(�)
⇥10�11
↵ = 0.1
I ↵ ⌧ 1 ! small-field model
I ↵ = 1 ! Starobinski
I ↵ � 1 ! m2�2/2
Model Choices
V (�) = V0
✓1 � e
�q
2
3↵�
MP
◆2
0 2 4 6 8 10 12
�/MP
0.0
0.2
0.4
0.6
0.8
1.0
V(�)
⇥10�10
↵ = 1.0
I ↵ ⌧ 1 ! small-field model
I ↵ = 1 ! Starobinski
I ↵ � 1 ! m2�2/2
Model Choices
V (�) = V0
✓1 � e
�q
2
3↵�
MP
◆2
0 5 10 15 20 25 30
�/MP
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
V(�)
⇥10�9
↵ = 100.0
I ↵ ⌧ 1 ! small-field model
I ↵ = 1 ! Starobinski
I ↵ � 1 ! m2�2/2
Model Choices
V (�) = V0
✓1 � e
�q
2
3↵�
MP
◆2
0 5 10 15 20 25 30 35
�/MP
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
V(�)
⇥10�8
↵ = 100000.0
I ↵ ⌧ 1 ! small-field model
I ↵ = 1 ! Starobinski
I ↵ � 1 ! m2�2/2
Cosmological Observables Encoded in ⇣ = � ln a
Cosmological Observables Encoded in ⇣ = � ln a
a ⌘ det(�ij)1/6
=
�aka
2
?�1/3
H ⌘ �1
3
� ijKij = �1
3
(K xx + 2K y
y )
Required Evolution
0 50 100 150 200
HI
x/p
3
�0.06
�0.04
�0.02
0.00
0.02
0.04
0.06
ln
a
0 50 100 150 200
HI
x/p
3
0.934
0.936
0.938
0.940
0.942
0.944
0.946
�/M
P
0 50 100 150 200
HI
x/p
3
0.000000
0.000002
0.000004
0.000006
0.000008
0.000010
0.000012
0.000014
H
+5.773⇥10�1
Initial Conditions (⌧ = 0)
Required Evolution
0 50 100 150 200
HI
x/p
3
0.112
0.114
0.116
0.118
0.120
0.122
0.124
0.126
�/M
P
0 50 100 150 200
HI
x/p
3
0.3750.3800.3850.3900.3950.4000.4050.4100.4150.420
H
End of Inflation (✏H = �d lnH/d ln a = 1)
Solving Einstein’s Equations for a Self-Gravitating Inflaton
Machine precision accuracy
IGauss-Legendre time-integrator (O(dt10), symplectic)
IFourier pseudospectral discretisation (exponential
convergence)
Fast to allow sampling
IAdaptive time-stepping
IAdaptive grid spacing
O(1s � 10s) to evolve through 60 e-folds of inflationMachine precision convergence and constraint preservations
Observational Constraints
Pr(A�,HILobs|C obs
2
, . . . ) / L(A�,HILobs)Pr(A�,HILobs| . . . )L = Pr(C obs
2
|A�,HILobs, . . . )
I A� : Fluctuation Amplitude P(k) / A2
�I HILobs : Uncertain post-inflation expansion history
I . . . : V (�), spectrum shape, IC hypersurface, Chigh�`2
, etc.
Planck measured C`
C obs
2
= 253.6µK 2 Chigh�`2
= 1124.1µK 2
Numerical GR Input
Pr
⇣ˆC2
|A�,HILobs, . . .⌘
Observer Weighting
Observer Weighting
Evaluation of CMB Quadrupole
ˆC2
=
1
5
⇣a(UL)
20
+ a(Q)
20
⌘2
+
Pm=2
m=�2,m 6=0
⇣a(Q)
2m
⌘2
�
a(Q)
2m : Gaussian with h(a(Q)
2m )
2i = 1124.1µK 2
Evaluation of CMB Quadrupole
ˆC2
=
1
5
⇣a(UL)
20
+ a(Q)
20
⌘2
+
Pm=2
m=�2,m 6=0
⇣a(Q)
2m
⌘2
�
a(Q)
2m : Gaussian with h(a(Q)
2m )
2i = 1124.1µK 2
�150 �100 �50 0 50 100 150
a(Q)
2m [µK]
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
P⇣ a
(Q
)
2m
⌘[(µK
)
�1
]
Evaluation of CMB Quadrupole
ˆC2
=
1
5
⇣a(UL)
20
+ a(Q)
20
⌘2
+
Pm=2
m=�2,m 6=0
⇣a(Q)
2m
⌘2
�
a(UL)
20
: NR simulations
�2 �1 0 1 2
a(UL)
20
/�(UL)
0
1
2
3
4
P⇣ a
(U
L)
20
/�(U
L)
|A�,.
..⌘
↵ =
2
300
A�
1 ⇥ 10
�5
3 ⇥ 10
�5
6 ⇥ 10
�5
Dependence of C2
on Model Parameters
0 1500 3000 4500
ˆC2
[µK
2
]
0.0
0.2
0.4
0.6
0.8
P⇣
ˆ C2
|A�,
�(UL)
�(Q
),.
..⌘
⇥10�3
�(UL)
�(Q) = 4
A�
1 ⇥ 10
�5
3 ⇥ 10
�5
6 ⇥ 10
�5
�2
dof=5
a(UL)
20
⇡ F (HILobs)2 @xpxp⇣ ⇡ F (HILobs)
2
1
ak@@x
⇣1
ak@x⇣
⌘
Dependence of C2
on Model Parameters
0 1500 3000 4500
ˆC2
[µK
2
]
0.0
0.2
0.4
0.6
0.8
P⇣
ˆ C2
|A�,
�(UL)
�(Q
),.
..⌘
⇥10�3
�(UL)
�(Q) = 4
A�
1 ⇥ 10
�5
3 ⇥ 10
�5
6 ⇥ 10
�5
�2
dof=5
a(UL)
20
⇡ F (HILobs)2 @xpxp⇣ ⇡ F (HILobs)
2
1
ak@@x
⇣1
ak@x⇣
⌘
Final Posterior
�5 �4 �3 �2 �1 0
log
10
(HI
Lobs
)
�6
�5
�4
�3log
10
(A
�)
↵ =
2
300
0
2
4
6
8
P(log
10
A�,l
og
10
HI
Lobs
|ˆ Cobs
2
)⇥10�4
Significant deviations from Gaussian approximation
Marginalised Constraints on Model Parameters: Amplitude
�6.0 �5.5 �5.0 �4.5 �4.0 �3.5 �3.0log
10
(A�)
0.00
0.02
0.04
0.06
0.08
0.10
P
⇣ log
10
(A
�)|ˆ C
obs
2
⌘
↵ =
2
300
Numerical GR
Gaussian
Heuristic Explanation: Initial Conditions
Heuristic Explanation: End-of-Inflation
Analytic Approximation
⇣ and comoving derivatives nearly Gaussian
�4 �3 �2 �1 0 1 2 3 4
⇣/��
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
P(⇣/�
�)
�4 �3 �2 �1 0 1 2 3 4
⇣ ��/����
0.000.050.100.150.200.250.300.350.400.45
P(⇣
��/�
���)
Treat as Gaussian random field
Large-Scale Approximation for a20
a20
(x0
) ⇡ Ae�2⇣(x0
)
�⇣ 00(x
0
) � ⇣ 0(x0
)
2
�
Analytic Approximation
⇣ and comoving derivatives nearly Gaussian
�4 �3 �2 �1 0 1 2 3 4
⇣/��
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
P(⇣/�
�)
�4 �3 �2 �1 0 1 2 3 4
⇣ ��/����
0.000.050.100.150.200.250.300.350.400.45
P(⇣
��/�
���)
Treat as Gaussian random field
Large-Scale Approximation for a20
a20
(x0
) ⇡ Ae�2⇣(x0
)
�⇣ 00(x
0
) � ⇣ 0(x0
)
2
�
Anaytic a20
Distributions
�1.5 �1.0 �0.5 0.0 0.5 1.0 1.5
a20
/��
0.0
0.5
1.0
1.5
2.0
2.5
3.0P
(a20
/�
�)
Vary �⇣ at fixed �⇣(p)/�⇣
Conclusions
INumerical relativity is a useful framework for making
cosmological predictions
ISometimes it is a necessary tool (deviations from Gaussianity)
IRobust qualitative conclusions over a variety of inflationary
models
IInflation is e↵ective at hiding large amplitude initial
fluctuations
IGaussianity of ⇣ in comoving coordinates suggests analytic
approach in 3D