1. 1 1 Dark Energy: Illuminating the Dark Eric Linder University of California, Berkeley Lawrence Berkeley National Lab

2. 2 2 Discovery! Acceleration

3. 3 3 Exploring Dark Energy New quantum physics? Does nothing weigh something? New gravitational physics? Is nowhere somewhere?

4. 4 4 Today’s Inflation Map the expansion history precisely and see the transition from acceleration to deceleration. Test the cosmology framework – alternative gravitation, higher dimensions, etc. SNAP constraints super acceleration

5. 5 5 Present Day Inflation Map the expansion history precisely and see the transition from acceleration to deceleration.

6. 6 6 Density History of the Universe Map the density history precisely, back to the matter dominated epoch.

7. 7 7 Mapping Our History The subtle slowing down and speeding up of the expansion, of distances with time: a(t), maps out cosmic history like tree rings map out the Earth’s climate history. STScI

8. 8 8 Cosmic Archaeology CMB: direct probe of quantum fluctuations Time: 0.003% of the present age of the universe. (When you were 0.003% of your present age, you were 2 cells big!) Supernovae: direct probe of cosmic expansion Time: 30-100% of present age of universe (When you were 12-40 years old) Cosmic matter structures: less direct probes of expansion Pattern of ripples, clumping in space, growing in time. 3D survey of galaxies and clusters - Lensing.

9. 9 9 The Universe: Early and Late Relic imprints of quantum particle creation in inflation - epoch of acceleration at 10 -35 s and energies near the Planck scale (a trillion times higher than in any particle acclerator). These ripples in energy density also occur in matter, as denser and less dense regions. Denser regions get a “head start” and eventually form into galaxies and clusters of galaxies. How quickly they grow depends on the expansion rate of the universe. It’s all connected!

10. 10 COBE WMAP Planck What do we see in the CMB? A view of the universe 99.997% of the way back toward the Big Bang - and much more. POLARBEAR has 2.5x the resolution and 1/5x the noise

11. 11 Geometry of Space WMAP/NASA/Tegmark CMB tells us about the geometry of space - flat? curved? But not much about evolution (snapshot) or dark energy (too early). Escher

12. 12 Type Ia Supernovae Exploding star, briefly as bright as an entire galaxy Characterized by no Hydrogen, but with Silicon Gains mass from companion until undergoes thermonuclear runaway Standard explosion from nuclear physics Insensitive to initial conditions: “Stellar amnesia” Höflich, Gerardy, Linder, & Marion 2003

13. 13 Standardized Candle Redshift tells us the expansion factor a Time after explosion Brightness Brightness tells us distance away (lookback time t)

14. 14 Standard Candles Brightness tells us distance away (lookback time) Redshift measured tells us expansion factor (average distance between galaxies)

15. 15 Discovering Supernovae

16. 16 Nearby Supernova Factory Understanding Supernovae High z: “Decelerating and Dustfree” HST Cycle 14, 219 orbits Supernova Properties Astrophysics G. Aldering (LBL) Cleanly understood astrophysics leads to cosmology

17. 17 Images Spectra Redshift & SN Properties dataanalysisphysics Nature of Dark Energy Each supernova is “sending” us a rich stream of information about itself. What makes SN measurement special? Control of systematic uncertainties

18. 18 Current Data: SNLS Supernova Legacy Survey: Rolling search on CFHT 2003-08; spectra First year results (71 SN), Astier et al. 2006 Expect total of 500-700 SN at z<0.95

19. 19 Current Data: SNLS Cosmological Constraints: consistent with  CDM But current data has no leverage on the dynamics, i.e. w. Analyses assume constant w. Astier et al. 2006Spergel et al. 2006

20. 20 Looking Back 10 Billion Years To see the most distant supernovae, we must observe from space. A Hubble Deep Field has scanned 1/25 millionth of the sky. This is like meeting 12 people and trying to understand the complexity of the entire US! STScI

21. 21 Looking Back 10 Billion Years STScI

22. 22 Dark Energy – The Next Generation SNAP: Supernova/Acceleration Probe Dedicated dark energy probe

23. 23 Design a Space Mission colorfulcolorful wide GOODS HDF ~10 4  Hubble Deep Field [SN] plus ~10 6  HDF [WL] deep Redshifts z=0-1.7 Exploring the last. 10 billion years 70% of the age of. the universe Both optical and near infrared wavelengths to see thru dust.

24. 24 New Technology Half billion pixel array 36 optical CCDs 36 near infrared detectors New technology LBNL CCDs Guider Spectrograph port Visible NIR Focus star projectors Calibration projectors JWST Field of View

25. 25 Astrophysical Uncertainties SystematicControl Host-galaxy dust extinction Wavelength-dependent absorption identified with high S/N multi-band photometry. Supernova evolutionSupernova subclassified with high S/N light curves and peak- brightness spectrum. Flux calibration errorProgram to construct a set of 1% error flux standard stars. Malmquist biasSupernova discovered early with high S/N multi-band photometry. K-correctionConstruction of a library of supernova spectra. Gravitational lensingMeasure the average flux for a large number of supernovae in each redshift bin. Non-Type Ia contamination Classification of each event with a peak-brightness spectrum. For accurate and precision cosmology, need to identify and control systematic uncertainties.

26. 26 Beyond Gaussian Gravitational lensing: Few hi z SN  poor PDF sampling Flux vs. magnitude bias Holz & Linder 2004 Extinction bias: One sided prior biases results Dust correction crucial; need NIR Linder & Miquel 2004 No extinction (perfect) Extinction correction W With AV bias With AV+RV bias Current data quality

27. 27 Controlling Systematics Same SN, Different z  Cosmology Same z, Different SN  Systematics Control

28. 28 Baryon Acoustic Oscillations The same primordial imprints in the photon field show up in matter density fluctuations. Baryon acoustic oscillations = patterned distribution of galaxies on very large scales (~150 Mpc). In the beginning... (well, 10-350,000 years after) It was hot. Normal matter was p +,e - – charged – interacting fervently with photons. This tightly coupled them, photon mfp << ct, and so they acted like a fluid. Density perturbations in one would cause perturbations in the other, but gravity was offset by pressure, so they couldn’t grow - merely oscillated. On the largest scales, set by the sound horizon, the perturbations were preserved. (CMB) Galaxy cluster size M. White

29. 29 - Standard ruler: we know the sound horizon by measuring the CMB; we measure the “wiggle” scale  distance - Like CMB is simple, linear physics – but require large, deep, galaxy redshift surveys (millions of galaxies, thousand(s) of deg 2 ) - Possibly WFMOS spectral or SNAP photometric survey - Complementary with SN if dark energy dynamic Baryon Acoustic Oscillations But... Observations give nonlinear, galaxy power spectrum in redshift space Theory predicts linear, matter power spectrum in real space (Plus selection effects of galaxy markers) Large scale power: mode coupling Bias Small scale power: velocity distortions

30. 30 SN factory SNLS Baryon Osc. SNAP HST Cluster SN Perlmutter

31. 31 Growth History of Structure While dark energy itself does not cluster much, it affects the growth of matter structure. Fractional density contrast  =  m /  m evolves as  + 2H  = 4  G  m  Sourced by gravitational instability of density contrast, suppressed by Hubble drag. Matter domination case:  ~ a -3 ~ t -2, H ~ (2/3t). Try  ~ t n. Characteristic equation n(n-1)+(4/3)n-(3/2)(4/9)=0. Growing mode n=+2/3, i.e.  ~ a...

32. 32 Gravitational Potential Poisson equation  2  (a)=4  Ga 2  m = 4  G  m (0) g(a) Growth rate of density fluctuations g(a) = (  m /  m )/a In matter dominated (hence decelerating) universe,  m /  m ~ a so g=const and  =const. By measuring the breakdown of matter domination we see the influence of dark energy. Direct count of growth - number of clusters vs. z [tough] Decay of potentials thru CMB ISW effect [cosmic variance] Effect of potentials on light rays - gravitational lensing

33. 33 Gravitational Lensing Gravity bends light… - we can detect dark matter through its gravity, - objects are magnified and distorted, - we can view “CAT scans” of growth of structure

34. 34 Gravitational Lensing “Galaxy wallpaper” Lensing by (dark) matter along the line of sight N. Kaiser

35. 35 Gravitational Lensing Lensing measures the mass of clusters of galaxies. By looking at lensing of sources at different distances (times), we measure the growth of mass. Clusters grow by swallowing more and more galaxies, more mass. Acceleration - stretching space - shuts off growth, by keeping galaxies apart. So by measuring the growth history, lensing can detect the level of acceleration, the amount of dark energy.

36. 36 Cluster Abundances Optical: light  mass Xray: hot gas  gravitational potential  mass Sunyaev-Zel’dovich: hot e - scatter CMB  mass Weak Lensing: gravity distorts images of background galaxies Traditional Difficult for z>1 Detects light, not mass Mass of what? Clean detections Difficult for z>1 Need optical survey for redshift Detects flux, not mass Only cluster center Assumes simple: ~n e 2 Clean detections Indepedent of redshift Need optical survey for redshift Detects flux, not mass Assumes ~simple: ~n e T e Detect mass directly Can go to z>1 Line of sight contamination Efficiency reduced Clusters -- largest bound objects. DE + astrophysics. Uncertainty in mass of 0.1 dex gives  w const ~0.1 [M. White],  w~?

37. 37 Cosmic Toolkit The Founder: Supernovae Ia distance-redshift The Players on the Field: SN Ia, Weak Lensing The main challenge will be control of systematics -- clean astrophysics to learn new physics Geometric Methods – “lightbulb”: a standard; don’t care how filament works (test it) SN Ia, SN II, Weak Lensing[CCC], Baryon Oscillations Geometry+Mass – “flashlight”: need to know about lens and battery [nonlinear mass distribution] Weak Lensing[structure], Strong Lensing Geometry+Mass+Gas – “torch”: need to know about the wood, flame, wind [hydrodynamics] SZ Effect, Cluster Counts