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

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 them?  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 them?  Let’s make some predictions.

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)  New window into 7<z<30 (e.g., Lyman-alpha)  Can we detect photons from early generation stars? What can we learn from these photons?  The signal is (almost) guaranteed, but measurement 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 stars? What can we learn from these photons?  The signal is (almost) guaranteed, but measurement is challenging because of contaminations due to:  Zodiacal light, and  Galaxies at z<6.

Near Infrared Background: Current Data vs Challenges  Extra-galactic infrared background in J and K bands over zodiacal light ~ 70 nW/m 2 /sr  These Measurements have been challenged.  Upper limits from blazar spectra: <14 nW/m 2 /sr (Aharonian et al. 2006)  Incomplete subtraction of Zodiacal light? ~15 nW/m 2 /sr (Wright 2001); <6 nW/m 2 /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/m 2 /sr  These Measurements have been challenged.  Upper limits from blazar spectra: <14 nW/m 2 /sr (Aharonian et al. 2006)  Incomplete subtraction of Zodiacal light? ~15 nW/m 2 /sr (Wright 2001); <6 nW/m 2 /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?  First stars?  Very massive (~1000 Msun), metal-free (Z=0) stars can explain the excess signal.  Santos, Bromm & Kamionkowski (2002); Salvaterra & Ferrara (2003)  Mini quasars?  Cooray & Yoshida (2004) studied the contribution 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); Salvaterra & Ferrara (2003)  Mini quasars?  Cooray & Yoshida (2004) studied the contribution from mini-quasars.  Madau & Silk (2005) showed that it would over-produce soft X-ray background.

Our Prediction: Fernandez & Komatsu (2006)  We don’t need metal-free stars!  Don’t be too quick to jump into conclusion that metal- free, 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) can produce nearly the same amount of excess light per SFR.  This is actually a good news: we don’t expect metal- free stars 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- free, 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) can produce nearly the same amount of excess light per SFR.  This is actually a good news: we don’t expect metal- free stars to dominate the near infrared background.  Why? Energy conservation.

Robust Calculation Unknown Can be calculated What we measure 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

The “Madau Plot” You don’t have to take this seriously for now. We need better measurements!

The 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?

Gaussianity vs Flatness (for fun)  Most people are generally happy that geometry of our Universe is flat. 1-  total = (+0.013, )  1-  total = (+0.013, ) (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? -54<f NL <114  For  G  f NL  G 2, -54<f NL <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-  total = (+0.013, )  1-  total = (+0.013, ) (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? -54<f NL <114  For  G  f NL  G 2, -54<f NL <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?  We need to know the predicted form of statistical tools as a function of model parameters to fit the data.  For  G  f NL  G 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  G  f NL  G 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

How Do They Look? Simulated temperature maps from f NL =0f NL =100 f NL =1000 f NL =5000

Is One-point PDF Useful? Conclusion: 1-point PDF is not very useful. (As far as CMB is concerned.) A positive f NL yields negatively skewed temperature anisotropy.

Bispectrum Constraints Komatsu et al. (2003); Spergel et al. (2006); Creminelli et al. (2006) (1yr) (3yr)

Trispectrum: Not For WMAP, But Perhaps Useful For Planck …  Trispectrum (~ f NL 2 )  Bispectrum (~ f NL )  Trispectrum (~ f NL 2 )  Bispectrum (~ f NL ) Kogo & Komatsu (2006)

The number of hot spots minus cold spots. Minkowski Functionals (MFs) V 1 : Contour Length V 0 :surface area V 2 : Euler Characteristic

MFs from WMAP (1yr) Komatsu et al. (2003); Spergel et al. (2006); Hikage et al. (2007) (3yr) AreaContour LengthGenus

Analytical formulae of MFs Gaussian term In weakly non-Gaussian fields (σ 0 <<1), the non- Gaussianity in MFs is characterized by three skewness parameters S (a). Perturbative formulae of MFs (Matsubara 2003) leading order of Non-Gaussian term Hikage, Komatsu & Matsubara (2006)

Surface area Contour Length Euler Characteristic θ s Comparison of MFs between analytical predictions and non-Gaussian simulations with f NL =100 at different Gaussian smoothing scales, θ s 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 difference ratio of MFs Hikage et al. (2007)

Expected 1σ errors on f NL from MFs of CMB for WMAP 8yr and Planck All WMAP 8-year and Planck observations should be sensitive to |f NL |~40 and 20, respectively, at the 68% confidence level.

Big Stuff from Gamma-ray Sky?

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  Astrophysical sources like blazars and clusters of galaxies cannot fully explain the observed CGB  Only 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 annihilation  Energy 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 CGB  Only 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 annihilation  Energy 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  Angular power spectrum, C l, 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, C l, 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 Equations Gamma-ray intensity: Spherical harmonic expansion: Limber’s equation:

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 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 ■ HETDEX, WFMOS (z=2-4) ■ CIP (z=3-6) ■ HETDEX, WFMOS (z=2-4) ■ CIP (z=3-6) Matter Power spectrum Cosmological Parameters Three Key Non-linear Effects Unlike 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

3 rd order Perturbation theory (PT) ■ Equations ■ Solving this equation perturbatively up to 3 rd order in δ. ■ The 3 rd order power spectrum is (e.g., Suto&Sasaki 1991; Jain&Bertschinger 1994) ■ Solving this equation perturbatively up to 3 rd order in δ. ■ The 3 rd order power spectrum is (e.g., Suto&Sasaki 1991; Jain&Bertschinger 1994)

PT Works Very Well! Z=4 z=1,2,3,4,5,6 from top to bottom Jeong & Komatsu (2006)

Rule of Thumb:  2 <0.4 Z=4 Jeong & Komatsu (2006)

Modeling Non-linear BAO Jeong & 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) How About GALAXY Power Spectrum?

PT Has Done It Again!

BAO Affected by Non-linear Bias But, now we know how to account for the non-linear bias.

Reionization & CMB - 21cm correlation Alvarez, Komatsu, Dore & Shapiro (2006) Doppler is a projected effect on CMB 21-cm maps result from 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 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 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 reionization

The Effect is Easy to Understand Reionization  positive correlation Recombination  negative correlation

21cm Anisotropy  To get cross-correlation between 21cm and Doppler, we need expression for spherical harmonic coefficients a lm : To leading order, the anisotropy is dependent on fluctuations in density and ionized fraction

Doppler Anisotropy To leading order, the Doppler anisotropy is dependent on fluctuations of velocity  density  Doppler arises from integral of velocity along line of sight Continuity equation  velocity fluctuation proportional to density fluctuation: We ignore fluctuations of density (Ostriker-Vishniac effect) and ionized fraction since they are higher order effects

Cross-correlation  Given the coefficients a lm for 21cm and Doppler, the cross-correlation can be found using  Shape of angular correlation same as linear power spectrum: C l ~P(k=l/r)  Evolution of the peak correlation amplitude (at l~100) with redshift  reionization history

Cross-correlation  The shape of the correlation traces the linear matter power spectrum at large scales (l~100)

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 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 RD MD CMB anisotropy Pulsar timing LISALIGO Entered the horizon during Energy-density Spectrum Primordial Gravitational:Usual Cartoon Picture

Numerical Solution: Traditional Flat?

Primordial Gravity Waves as a “Time Machine” in FRW spacetime in Minkowski spacetime Cosmological Redshift Therefore, the gravity wave spectrum is sensitive to the entire history of cosmic expansion after inflation.

Improving 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 Watanabe & Komatsu (2006)

Relativistic Degrees of Freedom: g * (T) In the early universe,  GW RDMD k RD g*(T)g*(T) T, k

Relativistic Degrees of Freedom: g * (T) Particle Contents: rest mass photon 0 neutrinos 0 e-, e+.51 MeV muon 106 MeV pions 140 MeV gluon 0 u quark 5 MeV d quark 9 MeV s quark 110 MeV c quark 1.3 GeV tauon 1.8 GeV b quark 4.4 GeV W bosons 80 GeV Z boson 91 GeV Higgs boson 114 GeV t 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 Asymptotic solution: 35.5% less! Anisotropic stress due to 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. damping Features in the Spectrum

Matter-radiation equality e + e - annihilation Neutrino decoupling QGP phase transition ElectroWeak P.T. SUSY breaking Reheating (10 14 GeV) GUT scale (10 16 GeV) Planck scale (10 19 GeV) CMB ~ Hz WMAP  GW0 < Plank  GW0 < Pulsar timing ~10 -8 Hz  GW0 < LISA ~10 -2 Hz  GW0 < DECIGO/BBO ~ 0.1 Hz  GW0 < ? Adv. LIGO ~10 2 Hz  GW0 < Detector sensitivitiesCosmological events Cosmological Events and Sensitivities

Summary 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 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.