Eiichiro Komatsu

University of Texas, Austin

February 23, 2007

Eiichiro Komatsu

University of Texas, Austin

February 23, 2007

Thinking about “Fun Stuff” from CIBER, Planck, GLAST,

HETDEX, SKA, and (beyond)LISA

Thinking about “Fun Stuff” from CIBER, Planck, GLAST,

HETDEX, SKA, and (beyond)LISA

Lucky Theoretical Cosmologists

Lucky Theoretical Cosmologists

“Data-dominated Era” The most joyful moment for theorists!

(Some of ) Their own predictions can actually be tested by observations within their lifetime.

Having many predictions is useful for maximizing the scientific outcome from (expensive) experiments. Are we exhausting all the possibilities? Are we getting the maximum information out of the data? Will we know we have surprises in the data when we see the

m?

Let’s make some predictions.

“Data-dominated Era” The most joyful moment for theorists!

(Some of ) Their own predictions can actually be tested by observations within their lifetime.

Having many predictions is useful for maximizing the scientific outcome from (expensive) experiments. Are we exhausting all the possibilities? Are we getting the maximum information out of the data? Will we know we have surprises in the data when we see the

m?

Let’s make some predictions.

Contents (7 minutes per topic)Contents (7 minutes per topic)

Cosmic Near Infrared Background (CIBER) Primordial Non-Gaussianity Updates (Planck) Dark Matter Annihilation (GLAST) Galaxy Power Spectrum (HETDEX) 21cm-CMB Correlation (SKA) Primordial Gravity Waves (LISA+)

Cosmic Near Infrared Background (CIBER) Primordial Non-Gaussianity Updates (Planck) Dark Matter Annihilation (GLAST) Galaxy Power Spectrum (HETDEX) 21cm-CMB Correlation (SKA) Primordial Gravity Waves (LISA+)

Why Study Cosmic Near Infrared Background? (1-4um)

Why Study Cosmic Near Infrared Background? (1-4um) New window into 7<z<30 (e.g., Lyman-alpha) Can we detect photons from early generation s

tars? What can we learn from these photons? The signal is (almost) guaranteed, but measure

ment is challenging because of contaminations due to: Zodiacal light, and Galaxies at z<6.

New window into 7<z<30 (e.g., Lyman-alpha) Can we detect photons from early generation s

tars? What can we learn from these photons? The signal is (almost) guaranteed, but measure

ment is challenging because of contaminations due to: Zodiacal light, and Galaxies at z<6.

Near Infrared Background: Current Data vs Challenges

Near Infrared Background: Current Data vs Challenges

Extra-galactic infrared background in J and K bands over zodiacal light ~ 70 nW/m2/sr

These Measurements have been challenged. Upper limits from blazar spectra: <14

nW/m2/sr (Aharonian et al. 2006) Incomplete subtraction of Zodiacal lig

ht? ~15 nW/m2/sr (Wright 2001); <6 nW/m2/sr (Thompson et al. 2006)

Let’s be open-minded. Clearly we need better data. Better

data will come from CIBER. What can we predict for the outcome of CIBER?

Extra-galactic infrared background in J and K bands over zodiacal light ~ 70 nW/m2/sr

These Measurements have been challenged. Upper limits from blazar spectra: <14

nW/m2/sr (Aharonian et al. 2006) Incomplete subtraction of Zodiacal lig

ht? ~15 nW/m2/sr (Wright 2001); <6 nW/m2/sr (Thompson et al. 2006)

Let’s be open-minded. Clearly we need better data. Better

data will come from CIBER. What can we predict for the outcome of CIBER?

Matsumoto et al. (2005)

“Excess”

Galaxy Contribution at z<6

Observed NIRB

Previous Study: Metal-free Stars, or Mini-quasars?

Previous Study: Metal-free Stars, or Mini-quasars?

First stars? Very massive (~1000 Msun), metal-free (Z=0)

stars can explain the excess signal. Santos, Bromm & Kamionkowski (2002); Salv

aterra & Ferrara (2003)

Mini quasars? Cooray & Yoshida (2004) studied the contributi

on from mini-quasars. Madau & Silk (2005) showed that it would over

-produce soft X-ray background.

First stars? Very massive (~1000 Msun), metal-free (Z=0)

stars can explain the excess signal. Santos, Bromm & Kamionkowski (2002); Salv

aterra & Ferrara (2003)

Mini quasars? Cooray & Yoshida (2004) studied the contributi

on from mini-quasars. Madau & Silk (2005) showed that it would over

-produce soft X-ray background.

Our Prediction: Fernandez & Komatsu (2006)

Our Prediction: Fernandez & Komatsu (2006)

We don’t need metal-free stars! Don’t be too quick to jump into conclusion that metal-fre

e, first stars have been seen in the NIRB. (Kashlinsky et al. 2005, 2007)

We don’t need anything too exotic. Stars contaminated by metals (say, Z=1/50 solar) ca

n produce nearly the same amount of excess light per SFR. This is actually a good news: we don’t expect metal-free s

tars to dominate the near infrared background. Why? Energy conservation.

We don’t need metal-free stars! Don’t be too quick to jump into conclusion that metal-fre

e, first stars have been seen in the NIRB. (Kashlinsky et al. 2005, 2007)

We don’t need anything too exotic. Stars contaminated by metals (say, Z=1/50 solar) ca

n produce nearly the same amount of excess light per SFR. This is actually a good news: we don’t expect metal-free s

tars to dominate the near infrared background. Why? Energy conservation.

Robust CalculationRobust Calculation

Unknown Can be calculated

What we measure

€

p(υ ,z)

= (M*c2) /Time × Efficiency

= ˙ ρ *(z)c 2 ∑α

eυα

€

Iυ =c

4π

p([1+ z]υ ,z)dz

H(z)(1+ z)∫

€

Iυ =c

4π

p([1+ z]υ ,z)dz

H(z)(1+ z)∫

€

eυα ≡

1

m*

dm mf (m)L υ

α (m)τ (m)

mc 2

⎡

⎣ ⎢

⎤

⎦ ⎥∫

Very simple argument:Luminosity per volume = (Stellar mass energy)

x(Radiation efficiency)/(Time during which radiation is emitted)

“Radiation Efficiency”

Stellar data from Schaller et al. (1992); Schaerer (2002)

NIRB Spectrum per SFR NIRB Spectrum per SFR

€

υIυ / ˙ ρ *

€

υIυ / ˙ ρ *

The “Madau Plot”The “Madau Plot”

You don’t have to take this seriously for now. We need better measurements!

The Future is in AnisotropyThe Future is in Anisotropy

Previous model (Kashlinsky et al. 2005; Cooray et al. 2006) ignored ionized bubbles.

We will use the reionization simulation (Iliev et al. 2006) to make simulated maps of the NIRB anisotropy: coming soon!

Previous model (Kashlinsky et al. 2005; Cooray et al. 2006) ignored ionized bubbles.

We will use the reionization simulation (Iliev et al. 2006) to make simulated maps of the NIRB anisotropy: coming soon!

How Do We Test Gaussianity of CMB?

How Do We Test Gaussianity of CMB?

Gaussianity vs Flatness (for fun)Gaussianity vs Flatness (for fun) Most people are generally happy that geometry of ou

r Universe is flat. 1-1-totaltotal=-0.003 (+0.013, -0.017)=-0.003 (+0.013, -0.017) (68% CL) (WMAP 3yr+

HST) Geometry of our Universe is consistent with being flat to

~3% accuracy at 95% CL.

What do we know about Gaussianity? For GfNLG

2, -54<f-54<fNLNL<114<114 (95% CL) (WMAP 3yr)

Primordial fluctuations are consistent with being Gaussian to ~0.001% accuracy at 95% CL.

Inflation is supported more by Gaussianity of primordial fluctuations than by flatness. ;-)

Most people are generally happy that geometry of our Universe is flat. 1-1-totaltotal=-0.003 (+0.013, -0.017)=-0.003 (+0.013, -0.017) (68% CL) (WMAP 3yr+

HST) Geometry of our Universe is consistent with being flat to

~3% accuracy at 95% CL.

What do we know about Gaussianity? For GfNLG

2, -54<f-54<fNLNL<114<114 (95% CL) (WMAP 3yr)

Primordial fluctuations are consistent with being Gaussian to ~0.001% accuracy at 95% CL.

Inflation is supported more by Gaussianity of primordial fluctuations than by flatness. ;-)

Are We Ready for Planck?Are We Ready for Planck? We need to know the predicted form of statistical tools

as a function of model parameters to fit the data. For GfNLG

2, there are only three statistical tools for which the analytical predictions are known: The angular bispectrum of

Temperature: Komatsu & Spergel (2001) Polarization: Babich & Zaldarriaga (2004) Joint Analysis Method (T+P): Yadav, Komatsu & Wandelt (2007)

The angular trispectrum Approximate Calculation (T+P): Okamoto & Hu (2002) Exact (T): Kogo & Komatsu (2006) Exact (P): N/A

Minkowski functionals Exact (T): Hikage, Komatsu & Matsubara (2006) Exact (P): N/A

We need to know the predicted form of statistical tools as a function of model parameters to fit the data.

For GfNLG2, there are only three statistical tools f

or which the analytical predictions are known: The angular bispectrum of

Temperature: Komatsu & Spergel (2001) Polarization: Babich & Zaldarriaga (2004) Joint Analysis Method (T+P): Yadav, Komatsu & Wandelt (2007)

The angular trispectrum Approximate Calculation (T+P): Okamoto & Hu (2002) Exact (T): Kogo & Komatsu (2006) Exact (P): N/A

Minkowski functionals Exact (T): Hikage, Komatsu & Matsubara (2006) Exact (P): N/A

How Do They Look?How Do They Look?

€

x( ) = ΦG x( ) + fNLΦG2 x( )Simulated temperature maps from

fNL=0 fNL=100

fNL=1000 fNL=5000

Is One-point PDF Useful?Is One-point PDF Useful?Conclusion: 1-point PDF is not very

useful. (As far as CMB is concerned.)

A positive fNL yields nega

tively skewed temperature anisotropy.

Bispectrum ConstraintsBispectrum Constraints

€

−58 < fNL <134(95%)

Komatsu et al. (2003); Spergel et al. (2006); Creminelli et al. (2006)

€

−54 < fNL <114(95%)

(1yr)

(3yr)

Trispectrum: Not For WMAP, But Perhaps Useful For Planck…Trispectrum: Not For WMAP,

But Perhaps Useful For Planck…

Trispectrum (~ fNL2)

Bispectrum (~ fNL)

Trispectrum (~ fNL2)

Bispectrum (~ fNL)

Kogo & Komatsu (2006)

The number of hot spots minus cold spots.

Minkowski Functionals (MFs)Minkowski Functionals (MFs)

V1: Contour LengthV0:surface area V2: Euler Characteristic

MFs from WMAPMFs from WMAP

€

fNL <137(95%)

€

−70 < fNL < 91(95%)(1yr)

Komatsu et al. (2003); Spergel et al. (2006); Hikage et al. (2007)

(3yr)

Area Contour Length Genus

polynomialHermiteth: Å]kHk

Analytical formulae of MFsAnalytical formulae of MFs

Gaussian term( )

( )( )

( ) ( ) ( ) ( ) ( )}6

1

36

1

{22

1

2002

)2()1(2

)0(

12/

0

1

2

22/)1(

2

σσννν

νσ

σ

ωω

ω

πν ν

OHSkk

HSk

HS

HeV

kkk

k

k

kkkk

+⎥⎦

⎤⎢⎣

⎡ −+++

⎟⎟⎠

⎞⎜⎜⎝

⎛=

−+

−−

−+

3/4,,1,1 3210 πωπωωω ====

( ) ( )[ ]∑ ++=l

llj

j WClll 22 1124

1σ kernelsmoothing:lW

In weakly non-Gaussian fields (σ0<<1) , the non-Gaussianity in MFs is characterized by three skewness parameters S(a).

)2,1,0( =k

Perturbative formulae of MFs (Matsubara 2003)

0,1,2)(a parameters skewness:)( =aS

leading order of Non-Gaussian term

Hikage, Komatsu & Matsubara (2006)

Surface area Contour Length Euler Characteristic

Comparison of MFs between analytical predictions and non-Gaussian simulations with fN

L=100 at different Gaussian smoothing scales, θθss

Analytical formulae agree with non-Gaussian simulations very well.

Simulations are done for WMAP; survey mask(Kp0 mask), noise pattern and antenna beam pattern

Comparison of analytical formulae with Non-Gaussian simulations

Comparison of analytical formulae with Non-Gaussian simulations

diff

eren

ce r

atio

of

MF s

Hikage et al. (2007)

Expected 1σ errors on fNL from MFs of CMB for WMAP 8yr and Planck

Expected 1σ errors on fNL from MFs of CMB for WMAP 8yr and Planck

All sθ

WMAP 8-year and Planck observations should be sensitive to |fN

L|~40 and 20, respectively, at the 68% confidence level.

Big Stuff from Gamma-ray Sky?

Dark matter (WIMP) annihilation

Dark matter (WIMP) annihilation

WIMP dark matter annihilates into gamma-ray photons.WIMP mass is likely around GeV–TeV, if WIMP is neutralino-like.Can GLAST see it?

WIMP dark matter annihilates into gamma-ray photons.WIMP mass is likely around GeV–TeV, if WIMP is neutralino-like.Can GLAST see it?

GeV-γ

CGB Anisotropy From Dark Matter Annihilation

CGB Anisotropy From Dark Matter Annihilation

Astrophysical sources like blazars and clusters of galaxies cannot fully explain the observed CGBOnly 25–50% using the latest blazar luminosity function

(Narumoto & Totani 2006)If dark matter annihilation contributes >30%, it should be detectable by GLAST in anisotropy.A smoking gun for dark matter annihilationEnergy spectrum of the mean intensity alone won’t be

convincing. We will need anisotropy data.

Astrophysical sources like blazars and clusters of galaxies cannot fully explain the observed CGBOnly 25–50% using the latest blazar luminosity function

(Narumoto & Totani 2006)If dark matter annihilation contributes >30%, it should be detectable by GLAST in anisotropy.A smoking gun for dark matter annihilationEnergy spectrum of the mean intensity alone won’t be

convincing. We will need anisotropy data.

Ando & Komatsu (2006); Ando, Komatsu, Narumoto & Totani (2006)

Predicting Angular Power Spectrum

Predicting Angular Power Spectrum

Angular power spectrum, Cl, is related to the spatial power spectrum via Limber’s equation.

We compute the 3D correlation from a “halo approach”: ST halo mass function, NFW density profile in each halo,

and Substructures included by the HOD

method.

Angular power spectrum, Cl, is related to the spatial power spectrum via Limber’s equation.

We compute the 3D correlation from a “halo approach”: ST halo mass function, NFW density profile in each halo,

and Substructures included by the HOD

method.

θ (= π / l)

Dark matter halo

A Few EquationsA Few EquationsGamma-ray intensity:

Spherical harmonic expansion:

Limber’s equation:

Predicted Angular Power Spectrum

Predicted Angular Power Spectrum

Ando, Komatsu, Narumoto & Totani (2006)

At 10 GeV for 2-yr observations of GLAST

Blazars (red curves) easily discriminated from the DM signal.

Galactic emission (foreground) is small at 10 GeV

At 10 GeV for 2-yr observations of GLAST

Blazars (red curves) easily discriminated from the DM signal.

Galactic emission (foreground) is small at 10 GeV

S/N Somewhat Sensitive to What We Assume For

Substructures

S/N Somewhat Sensitive to What We Assume For

Substructures

Our Best Guess:Our Best Guess:

“If dark matter annihilation contributes > 30% of the CGB, GLAST should be able to detect anisotropy.”

Toward Precision Modeling of Galaxy Power Spectrum for High-z Galaxy Surveys

Toward Precision Modeling of Galaxy Power Spectrum for High-z Galaxy Surveys

■ HETDEX, WFMOS (z=2-4)

■ CIP (z=3-6)

■ HETDEX, WFMOS (z=2-4)

■ CIP (z=3-6) sn

Matter Power spectrum Cosmological Parameters

kd

nd s

ln

lnΛ

Λ=ρp

wdz

dw

Three Key Non-linear EffectsThree Key Non-linear EffectsUnlike CMB, the large-scale structure is pretty non-linear.

The main non-linear effects to account for are:

■ Nonlinear growth of the density field (Jeong&Komatsu 2006)

■ Nonlinear bias (Jeong&Komatsu, in prep.)

■ Nonlinear Redshift space distortion (work in progress)

Method: Use 3rd-order Perturbation Theory

3rd order Perturbation theory (PT)3rd order Perturbation theory (PT)

■ Equations■ Equations

■ Solving this equation perturbatively up to 3rd order in δ.■ The 3rd order power spectrum is

(e.g., Suto&Sasaki 1991; Jain&Bertschinger 1994)

■ Solving this equation perturbatively up to 3rd order in δ.■ The 3rd order power spectrum is

(e.g., Suto&Sasaki 1991; Jain&Bertschinger 1994)

PT Works Very Well!PT Works Very Well!

Z=4

z=1,2,3,4,5,6 from top to bottom

Jeong & Komatsu (2006)

Rule of Thumb: 2<0.4Rule of Thumb: 2<0.4

Z=4

Jeong & Komatsu (2006)

Modeling Non-linear BAOModeling Non-linear BAOJeong & Komatsu (2006)

■ Relation between galaxies and underlying density:■ Assumption: galaxy formation is a local process

■ 3rd-order PT calculation gives the PT galaxy power spectrum (Heavens et al. 1998)

■ Relation between galaxies and underlying density:■ Assumption: galaxy formation is a local process

■ 3rd-order PT calculation gives the PT galaxy power spectrum (Heavens et al. 1998)

€

δh (r x ) = b0 + b1δ(

r x ) +

b2

2δ 2(

r x ) +

b3

6δ 3(

r x )

How About GALAXY Power Spectrum?How About GALAXY Power Spectrum?

PT Has Done It Again!PT Has Done It Again!PT Has Done It Again!PT Has Done It Again!

BAO Affected by Non-linear BiasBAO Affected by Non-linear Bias

But, now we know how to account for the non-linear bias.

Reionization & CMB - 21cm correlationAlvarez, Komatsu, Dore & Shapiro (2006)

Doppler is aprojected

effect on CMB

21-cm maps resultfrom line-emission

Doppler effect comes from peculiar velocity along l.o.s.

21-cm fluctuations due to density and ionized fraction

We focus on degree angular scales

Doppler effect comes from peculiar velocity along l.o.s.

21-cm fluctuations due to density and ionized fraction

We focus on degree angular scales

21cm x CMB Doppler21cm x CMB Doppler

21cm lines Produced by neutral hydrogen during reionization As reionization proceeds, 21cm slowly dissappears – morphology of

reionization imprinted on 21cm anisotropy Because it is line emission, redshift frequency

CMB Doppler effect

Free electrons during reionization scatter CMB photons Electrons moving towards us blueshift hot spot Electrons moving away from us redshift cold spot

Doppler effect is example of “secondary anisotropy” in CMB

Both effects are sensitive to reionizationBoth effects are sensitive to reionization

21cm lines Produced by neutral hydrogen during reionization As reionization proceeds, 21cm slowly dissappears – morphology of

reionization imprinted on 21cm anisotropy Because it is line emission, redshift frequency

CMB Doppler effect

Free electrons during reionization scatter CMB photons Electrons moving towards us blueshift hot spot Electrons moving away from us redshift cold spot

Doppler effect is example of “secondary anisotropy” in CMB

Both effects are sensitive to reionizationBoth effects are sensitive to reionization

The Effect is Easy to UnderstandThe Effect is Easy to Understand

• Reionization positive correlation• Recombination negative correlation

Probing Reionization History Cross-correlation peaks when ionized fraction about a half Sign and amplitude of correlation constrains derivative of ionized

fraction Typical signal amplitude ~500 (K)2

Above expected error from Square Kilometer Array for ~1 year of observation ~135 (K)2

Cross-correlation peaks when ionized fraction about a half Sign and amplitude of correlation constrains derivative of ionized

fraction Typical signal amplitude ~500 (K)2

Above expected error from Square Kilometer Array for ~1 year of observation ~135 (K)2

Our Prediction for SKAOur Prediction for SKA

The SKA data should be correlated with CMB, and WMAP data are good enough!

It is even plausible that the first convincing evidence for 21-cm from reionization would come from the cross-correlation signal. Systematic errors, foregrounds, or unaccounted

noise won’t produce the cross-correlation, but will produce spurious signal in the auto-correlation.

The SKA data should be correlated with CMB, and WMAP data are good enough!

It is even plausible that the first convincing evidence for 21-cm from reionization would come from the cross-correlation signal. Systematic errors, foregrounds, or unaccounted

noise won’t produce the cross-correlation, but will produce spurious signal in the auto-correlation.

GW(k)

k

~k-2

RDMD

CMB anisotropy

Pulsar timing

LISA LIGO

Entered the horizon during

Energy-density Spectrum Primordial Gravitational: Usual Cartoon Picture

Numerical Solution: TraditionalNumerical Solution: Traditional

Hz1016.3Gyr1

72.0

1 ,1015.4

171

52

−−

−−

×=

=−=×=

hh RMR

Flat?

€

GW 0 10−10 E inf

1016GeV

⎛

⎝ ⎜

⎞

⎠ ⎟4

Primordial Gravity Waves as a “Time Machine”

Primordial Gravity Waves as a “Time Machine”

€

ds2 = a2(τ )[−dτ 2 + (δ ij + hij )dx idx j ]

hij = 0 ⇒ ˙ ̇ h ij + 2˙ a

a˙ h ij + k 2hij = 0 in FRW spacetime

0 0

)( 2

22

=+⇒=

++−=

ijijij

jiijij

hkhh

dxdxhdtds&&

δ

in Minkowski spacetime

Cosmological Redshift

Therefore, the gravity wave spectrum is sensitive to the entire history of cosmic expansion after inflation.

Improving CalculationsImproving Calculations• Change in the background expansion law Relativistic Degrees of Freedom: g*(T)

Radiation Content of the Early Universe

• Neutrino physics Neutrino Damping (J. Stewart 1972, Rebhan & Schwarz 1994, Weinberg 2004, Dicus & Repko 2005 ) Collisionless Damping due to Anisotropic Stress

€

˙ ̇ h ij + 2˙ a

a˙ h ij + k 2hij =16πGπ ij

4

0

3/1

0*

*20

2

3

8−−

⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛==

aa

gg

HG

H Rρπ

Watanabe & Komatsu (2006)

Relativistic Degrees of Freedom: g*(T)

€

ρrad = ρ i =i

∑ π 2

30g*(T)T 4 / ∝ a−4,

but ∝ g*−1/ 3a−4

In the early universe, L↔↔↔ −+ ννγγ ee

?4−∝ aradρGW

RDMD

kRD

g*(T)

T, k

€

GW =ρGW ,0

ρ rad ,0

≠ const., but ∝g*(Thc )

g*0

⎛

⎝ ⎜

⎞

⎠ ⎟

−1/ 3

Relativistic Degrees of Freedom: g*(T)

Particle Contents: rest massphoton 0neutrinos 0e-, e+ .51 MeVmuon 106 MeVpions 140 MeVgluon 0u quark 5 MeVd quark 9 MeVs quark 110 MeVc quark 1.3 GeVtauon 1.8 GeVb quark 4.4 GeVW bosons 80 GeVZ boson 91 GeVHiggs boson 114 GeVt quark 174 GeV

SUSY ?~1TeV

QGP P.T.~180MeV

e-,e+ ann.~510keV

Collisionless Damping of GW by Anisotropic Stress due to Neutrino Free-streaming

543tot

0

2

2

sin3cos3sin)( , ,

,)()()(

)()(24)()(

)(

)(2)(

0 ,0

162

1 s

s

s

s

s

ssKf

a

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dUuhUuKua

uaufuhuh

ua

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ah

t

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u

ijijijij

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ijijijij

+−−≡≡≡

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ν

&&&&

Asymptoticsolution:

645.0||

0 ,8031.0 ,)sin(

)0()(

2=∝⇒

==−

→

A

Auu

Ahuh

GW

ijij δδ

35.5% less!

Anisotropic stress due to ν free-streaming couples with GWs

The Most Accurate Spectrum of GW in the Standard Model of Particle Physics

Watanabe & Komatsu (2006)

Old Result

e-,e+ ann. QGP P.T.

ν damping

Features in the SpectrumFeatures in the Spectrum

Matter-radiation equalitye+e- annihilationNeutrino decoupling QGP phase transition ElectroWeak P.T.SUSY breaking

Reheating (1014 GeV)GUT scale (1016 GeV)Planck scale (1019 GeV) Hz

Hz

Hz

Hz

Hz

Hz

Hz

Hz

Hz

11

9

7

3

4

7

10

10

16

10

10

10

10

10

10

10

10

10

−

−

−

−

−

−CMB ~10-18 Hz WMAP GW0 < 10-11

Plank GW0 < 10-13

Pulsar timing ~10-8 Hz GW0 < 10-8

LISA ~10-2 Hz GW0 < 10-11

DECIGO/BBO ~ 0.1 Hz GW0 < ?Adv. LIGO ~102 Hz GW0 < 10-10

Detector sensitivitiesCosmological events

Hz100

)(

GeV10

6/1

hc*hc60 ⎟

⎠

⎞⎜⎝

⎛⎟⎠

⎞⎜⎝

⎛≅ − TgTf

Cosmological Events and SensitivitiesCosmological Events and Sensitivities

Summary of Our PredictionsSummary of Our Predictions Cosmic Near Infrared Background (CIBER)

The signal will not come from metal-free stars, but will come primarily from stars with metals. Primordial Non-Gaussianity Updates (Planck)

We are ready for Planck (bispectrum/trispectrum/MFs). Dark Matter Annihilation (GLAST)

GLAST should detect DM annihilation if DM is neutralino-like and contributes >30% of the gamma-ray background intensity.

Galaxy Power Spectrum (HETDEX) Non-linear bias is important for BAO. We know how to handle it.

21cm-CMB Correlation (SKA) SKA data should be correlated with WMAP data at degree scales.

Primordial Gravity Waves (LISA+) GW spectrum won’t be featureless, but will be with full of features.

Cosmic Near Infrared Background (CIBER) The signal will not come from metal-free stars, but will come primarily from stars with metals.

Primordial Non-Gaussianity Updates (Planck) We are ready for Planck (bispectrum/trispectrum/MFs).

Dark Matter Annihilation (GLAST) GLAST should detect DM annihilation if DM is neutralino-like and contributes >30% of the ga

mma-ray background intensity. Galaxy Power Spectrum (HETDEX)

Non-linear bias is important for BAO. We know how to handle it. 21cm-CMB Correlation (SKA)

SKA data should be correlated with WMAP data at degree scales. Primordial Gravity Waves (LISA+)

GW spectrum won’t be featureless, but will be with full of features.