1 / 70

Probing Dark Energy

Probing Dark Energy. Josh Frieman. PASCOS, Ohio State University, Sept. 10, 2006. Dark Energy and the Accelerating Universe. Brightness of distant Type Ia supernovae, along with CMB and galaxy clustering data, indicates the expansion of the Universe

milt
Télécharger la présentation

Probing Dark Energy

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Probing Dark Energy Josh Frieman PASCOS, Ohio State University, Sept. 10, 2006

  2. Dark Energy and the Accelerating Universe Brightness of distant Type Ia supernovae, along with CMB and galaxy clustering data, indicates the expansion of the Universe is accelerating, not decelerating. This requires either a new form of stress-energy with negative effective pressureor a breakdown of General Relativity at large distances: DARK ENERGY Characterize by its effective equation of state: w = p/<1/3 and its relative contribution to the present density of the Universe:DE Special case:cosmological constant:w = 1

  3. What is the Nature of the Dark Energy? Stress-Energy:G = 8G [T(matter) + T(dark energy)] Gravity:G + f(g) = 8G T(matter) (e.g., branes) Inhomogeneity: Key Experimental Questions: Is DE observationally distinguishable from a cosmological constant, for which T (vacuum) = g/3, i.e., w =—1? Can we distinguish between gravity and stress-energy? Combine geometric with structure-growth probes Does dark energy evolve: w=w(z)?

  4. Probing Dark Energy • Probe dark energy through the history of the expansion rate: and the growth of large-scale structure: • Parametrize DE Evolution: • Geometric tests: • Comoving distance Weak Lensing • Standard Candles Supernovae • Standard Rulers Baryon Oscillations • Standard Population Clusters

  5. Assuming flat Universe and wa=0 Constraints on Constant Dark Energy Equation of State CFHT SNLS+ SDSS BAO Astier etal 05 Eisenstein etal 05

  6. Constraints on Time-varying Dark Energy 3-parameter Model Substantially weaker Jarvis etal 05 Assumes flat Universe

  7. Scalar Field Dark Energy • If Dark Energy is due to a scalar field, j, evolving in a potential, V(j): • Density & pressure: V(j) j

  8. Scalar Field Dark Energy aka quintessence General features: meff < 3H0 ~ 10-33 eV (w < 0) (Potential < Kinetic Energy) V ~ m22 ~ crit ~ 10-10 eV4  ~ 1028 eV ~ MPlanck V(j) (10–3 eV)4 j 1028 eV Ultra-light particle: Dark Energy hardly clusters, nearly smooth Equation of state: usually, w > 1 and evolves in time Hierarchy problem: Why m/ ~ 1061? Weak coupling: Quartic self-coupling  < 10122

  9. The Coincidence Problem Why do we live at the `special’ epoch when the dark energy density is comparable to the matter energy density? matter ~ a-3 DE~ a-3(1+w) a(t) Today

  10. Scalar Field Models & Coincidence `Dynamics’ models (Freezing models) `Mass scale’ models (Thawing models) V V e.g., e– or –n   MPl Runaway potentials DE/matter ratio constant (Tracker Solution) Pseudo-Nambu Goldstone Boson Low mass protected by symmetry (Cf. axion)JF, Hill, Stebbins, Waga V() = M4[1+cos(/f)] f ~ MPlanck M ~ 0.001 eV ~ m Ratra & Peebles; Caldwell, Steinhardt,etal; Albrecht etal,…

  11. Goal for ~2012: SPT+DES Goal for ~2015+: JDEM, LSST Caldwell & Linder

  12. Probing Dark Energy Primary Techniques identified by the Dark Energy Task Force report: Supernovae Galaxy Clusters Weak Lensing Baryon Acoustic Oscillations Multiple Techniques needed: complementary in systematics and in science reach

  13. Probing Dark Energy Primary Techniques identified by the Dark Energy Task Force report: Supernovae Galaxy Clusters Weak Lensing Baryon Acoustic Oscillations Multiple Techniques needed: complementary in systematics and in science reach

  14. Type Ia SN Peak Brightness as a calibrated `Standard’ Candle Peak brightness correlates with decline rate Phillips 1993 After correction, ~ 0.15 mag (~7% distance error) Luminosity Time

  15. Supernova Hubble Diagram CFHT Supernova Legacy Survey Astier etal 05 Needed: more, better data at low and Intermediate redshift KAIT, SNF, CSP, CfA SDSS ESSENCE, SNLS

  16. Published Light Curves for Nearby Supernovae More, Better needed

  17. Supernovae Cf. Y.B. On-going SN surveys (200) Future Surveys: PanSTARRS, DES, JDEM, LSST (2000) (3000) (105) high-z

  18. Supernovae: the JDEM Future • Goal: Determine w0 to ~5% and wa to ~20% (combined with CMB) • Statistical Requirement: ~1% relative distance measurements (2% flux) in z~0.1 redshift bins • Assume systematic error can be reduced to this level Kim, etal 04, Kim & Miquel 05 • Require ~3000 SNe spread over z ~ 0.3-1.7 and a well-observed sample at low z to anchor the Hubble diagram. Consequent requirements for NIR imaging and photometric stability lead to a space-based mission. Proposals: SNAP, DESTINY, JEDI,…

  19. Probing Dark Energy Evolution: 2% Mag Systematic Error Floors 3000 SNe JF, Huterer, Linder, Turner 03

  20. Can we get there? Systematics Concerns e.g., Luminosity Evolution: We believe SNe Ia at z~0.5 are not intrinsically ~25% fainter than nearby SNe (the basis for Dark Energy). Could SNe at z~1.5 be 2% fainter/brighter than those nearby, in a way that leaves all other observables fixed?Key: Many observables per SN; which needed? Expectation:drift in progenitor population mix (progenitor mass, age, metallicity, C/O, accretion rates, etc). Control:the variety of host environments at low redshift spans a larger range of metallicity, environment, than the median differences between low- and high-z environments, so we can compare high-z apples with low-z apples, using host info., LC shape, colors, spectral features & spectral evolution, and assuming these exhaust the parameters that control Lpeak. Not (yet) guaranteed by SN theory

  21. SDSS II Supernova SurveySept-Nov. 2005-7 • Obtain ~200 high-quality SNe Ia light curves in the `redshift desert’ z~0.05-0.35: continuous Hubble diagram • Probe Dark Energy in z regime less sensitive to evolution than, and complementary to, deeper surveys • Study SN Ia systematics with high photometric accuracy

  22. SDSS 2.5 meter Telescope

  23. SN 2005 gb Composite gri images Before After z = 0.086, confirmed at ARC 3.5m Preliminary gri light curve and fit from low-z templates

  24. SDSS II: ~130 spectroscopically confirmed Type Ia Supernovae from the Fall 2005 Season First Results aiming for Jan. 07 AAS

  25. Unusual SN: 2005gj • Followed this object all semester with MDM • 12 observations • Type Ia strongly interacting with CSM • Only 1 other object like this • 2002ic • Prieto et al. 2006 (in preparation) • Spitzer observations

  26. Probing Dark Energy Primary Techniques identified by the Dark Energy Task Force report: Supernovae Galaxy Clusters Weak Lensing Baryon Acoustic Oscillations Multiple Techniques needed: complementary in systematics and in science reach

  27. Evolution of Structure Robustness of the paradigm recommends its use as a Dark Energy probe Price:additional cosmological and structure formation parameters Bonus:additional structure formation Parameters Methods: WL, Clusters

  28. Growth of Density Perturbations Volume Element w = –1 Flat, matter-dominated w = -0.7 Raising w at fixed WDE: decreases growth rate of density perturbations and decreases volume surveyed

  29. Clusters and Dark Energy Number of clusters above observable mass threshold • Requirements • Understand formation of dark matter halos • Cleanly select massive dark matter halos (galaxy clusters) over a range of redshifts • Redshift estimates for each cluster • Observable proxy that can be used as cluster mass estimate: O =g(M) Primary systematic: Uncertainty in bias & scatter of mass-observable relation Dark Energy equation of state Volume Growth (geometry) Mohr

  30. Clusters and Dark Energy Number of clusters above observable mass threshold • Requirements • Understand formation of dark matter halos • Cleanly select massive dark matter halos (galaxy clusters) over a range of redshifts • Redshift estimates for each cluster • Observable proxy that can be used as cluster mass estimate: O =g(M) Primary systematic: Uncertainty in bias & scatter of mass-observable relation Dark Energy equation of state Volume Growth (geometry) Mohr

  31. Clusters form hierarchically z = 7 dark matter z = 5 z = 3 time z = 0.5 z = 0 z = 1 Kravtsov 5 Mpc

  32. Warren et al ‘05 Theoretical Abundance of Dark Matter Halos Warren etal

  33. Clusters and Dark Energy Number of clusters above observable mass threshold • Requirements • Understand formation of dark matter halos • Cleanly select massive dark matter halos (galaxy clusters) over a range of redshifts • Redshift estimates for each cluster • Observable proxy that can be used as cluster mass estimate: O =g(M) Primary systematic: Uncertainty in bias & scatter of mass-observable relation Dark Energy equation of state Volume Growth (geometry) Mohr

  34. 4 Techniques for Cluster Selection: Optical galaxy concentration Weak Lensing Sunyaev-Zel’dovich effect (SZE) X-ray Cluster Selection

  35. Holder

  36. Clusters and Dark Energy Number of clusters above observable mass threshold • Requirements • Understand formation of dark matter halos • Cleanly select massive dark matter halos (galaxy clusters) over a range of redshifts • Redshift estimates for each cluster • Observable proxy that can be used as cluster mass estimate: O =g(M) Primary systematic: Uncertainty in bias & scatter of mass-observable relation Dark Energy equation of state Volume Growth (geometry) Mohr

  37. Photometric Redshifts Elliptical galaxy spectrum • Measure relative flux in four filters griz: track the 4000 A break • Estimate individual galaxy redshifts with accuracy (z) < 0.1 ~0.02 for clusters • Precision is sufficient for Dark Energy probes, provided error distributions well measured.

  38. Galaxy Photo-z Simulations DES griz filters +VDES JK DES DES + VDES on ESO VISTA 4-m enhances science reach 10 Limiting Magnitudes g 24.6 r 24.1 i 24.0 z 23.9 +2% photometric calibration error added in quadrature Key: Photo-z systematic errors under control using existing spectroscopic training sets to DES photometric depth Cunha, etal Improved Photo-z & Error Estimates and robust methods of outlier rejection

  39. Clusters and Dark Energy Number of clusters above observable mass threshold • Requirements • Understand formation of dark matter halos • Cleanly select massive dark matter halos (galaxy clusters) over a range of redshifts • Redshift estimates for each cluster • Observable proxy that can be used as cluster mass estimate: O =g(M) Primary systematic: Uncertainty in bias & scatter of mass-observable relation Dark Energy equation of state Volume Growth (geometry) Mohr

  40. Effect of Uncertainty in mass-observable relation Sensitivity to Mass Threshold Precision Cosmology with Clusters? Mass threshold

  41. 4 Techniques for Cluster Mass Estimation: Optical galaxy concentration Weak Lensing Sunyaev-Zel’dovich effect (SZE) X-ray Cross-compare these techniques to reduce systematic errors Additional cross-checks: shape of mass function; cluster correlations Cluster Mass Estimates

  42. SZE vs. Cluster Mass: Progress toward Realistic Simulations Adiabatic ∆ Cooling+Star Formation SZE flux small (~10%) scatter SZE Observable Kravtsov Nagai Integrated SZE flux decrement depends only on cluster mass: insensitive to details of gas dynamics/galaxy formation in the cluster core robust scaling relations Motl, etal

  43. Gravitational Lensing by Clusters

  44. Weak Lensing of Faint Galaxies: distortion of shapes Background Source shape

  45. Weak Lensing of Faint Galaxies: distortion of shapes Foreground Cluster Background Source shape Note: the effect has been greatly exaggerated here

  46. Lensing of real (elliptically shaped) galaxies Foreground Cluster Background Source shape Co-add signal around a number of Clusters

  47. Statistical Weak Lensing by Galaxy Clusters Mean Tangential Shear Profile in Optical Richness (Ngal) Bins to 30 h-1Mpc Sheldon, Johnston, etal SDSS

More Related