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Transcript of Eiichiro Komatsu University of Texas, Austin February 23, 2007 Eiichiro Komatsu University of Texas,...
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
dtku
dUuhUuKua
uaufuhuh
ua
uauh
Ghkha
ah
t
t
u
ijijijij
ijiii
ijijijij
+−−≡≡≡
′−⎟⎟⎠
⎞⎜⎜⎝
⎛ ′−=+′
′+′′
=∂=
=++
∫
∫
ρρ
ππ
ππ
νν
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.