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KDUST 宇宙学研讨会 国台， 2009.12.16

KDUST 暗能量研究. 詹虎 及张新民、范祖辉、赵公博等人. KDUST 宇宙学研讨会 国台， 2009.12.16. Systematics of Dark Energy Probes. Type Ia Supernova Luminosity evolution, Galactic & host-galaxy dust extinction, contamination. Weak lensing Shear calibration: Properties of additive & multiplicative shear errors?

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KDUST 宇宙学研讨会 国台， 2009.12.16

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  1. KDUST暗能量研究 詹虎 及张新民、范祖辉、赵公博等人 KDUST 宇宙学研讨会 国台，2009.12.16

  2. Systematics of Dark Energy Probes • Type Ia Supernova • Luminosity evolution, Galactic & host-galaxy dust extinction, contamination. • Weak lensing • Shear calibration: Properties of additive & multiplicative shear errors? • Photo-zs: What is the error distribution function? How and how well can we calibrate it? What is the impact of non-Gaussian photo-z errors on cosmological constraints? How about catastrophic redshift errors? • Nonlinear evolution: Percent-level calibration of the nonlinear power spectrum at k < 1 h/Mpc? Baryonic influence on the dark matter distribution? • Intrinsic alignment: Local & large-scale, intrinsic—intrinsic, gravitational—intrinsic alignments. How to remove/model the effects? • Baryon Acoustic Oscillations • Nonlinear evolution: Shift of the BAO scale? Higher-order statistics? Parameter estimation from non-Gaussian data? • Galaxy bias: Scale dependence? luminosity dependence? • Redshift distortion (spectroscopic BAO): Accurate calibration with N-body simulations? • Cluster Counting • Mass—observable relation: mean & variance? KDUST宇宙学

  3. WL Shear Systematics Current best shear estimators can achieve multiplicative error (shear calibration error) of < 1% and residual shear of ~ 0.0001. Our forecasts for future surveys assume <m> ~ 0.5% and <c> ~ 10-5. KDUST宇宙学

  4. WL Shear Systematics Parameter constraints Degradations due to shear errors are not bound (no self-calibration from WL itself). KDUST宇宙学

  5. Uncertainty of Photo-z Error Distribution KDUST宇宙学

  6. Uncertainty of Photo-z Error Distribution KDUST宇宙学

  7. Dome A Advantages • Good seeing: point sources, high-redshift objects, high-resolution imaging, source counts, shape measurement • Infrared: high-redshift objects, photometric redshifts, low shape noise • LONG night: time domain Dome A site is advantageous for controlling systematic errors of cosmological probes, which is critical to the success of future surveys. KDUST宇宙学

  8. Dome A Advantages Slide from Jason Rhodes KDUST宇宙学

  9. Simulation of Residual Shear KDUST宇宙学

  10. Photo-z Sys Effects on DE Constraints Abdalla et al. (2008) Zhan et al. arXiv:0902.2599 A joint analysis of the shear and galaxy overdensities for the same set of galaxies involves galaxy—galaxy, galaxy—shear, and shear—shear correlations, which enable some calibration of systematics that would otherwise adversely impact each probe. While the WL constraints on the dark energy equation of state (EOS, w = p/r) parameters, w0 and wa, as dened by w = w0+wa(1-a), are sensitive to systematic uncertainties in the photo-z error distribution, the joint BAO and WL results remain fairly immune to these systematics. KDUST宇宙学

  11. Impact of Systematics on DE Constraints Slide from Tony Tyson KDUST宇宙学 Zhan et al. arXiv:0902.2599

  12. KDUST—LSST Synergy KDUST宇宙学

  13. KDUST Site Performance Without consideration for hardware or survey KDUST site assumptions: n(z) ~ z2exp(-z/0.6) (peaks at z=1.2) Photo-z rms: sz=0.03(1+z) (ugrizyJH) Photo-z bias prior: sP(dz)=0.2sz Shear calibration error: ±0.002 Residual shear power: 4×10-10 KDUST宇宙学

  14. KDUST Site Performance Without consideration for hardware or survey KDUST site assumptions: n(z) ~ z2exp(-z/0.6) (peaks at z=1.2) Photo-z rms: sz=0.03(1+z) (ugrizyJH) Photo-z bias prior: sP(dz)=0.2sz Shear calibration error: ±0.002 Residual shear power: 4×10-10 LSST site assumptions: n(z) ~ z2exp(-z/0.5) (peaks at z=1) Photo-z rms: sz=0.05(1+z) Photo-z bias prior: sP(dz)=0.3sz Shear calibration error: ±0.005 Residual shear power: 10-9 KDUST宇宙学

  15. KDUST—LSST Synergy LSST 20,000 sq. deg. ugrizy n(z) ~ z2exp(-z/0.5) Photo-z rms: sz=0.05(1+z) Photo-z bias prior: sP(dz)=0.3sz Shear calibration error: ±0.005 Residual shear power: 10-9 KDUST宇宙学

  16. KDUST—LSST Synergy KDUST JH + LSST ugrizy 5000 sq. deg.: n(z) ~ z2exp(-z/0.6) Photo-z rms: sz=0.03(1+z) Photo-z bias prior: sP(dz)=0.2sz Shear calibration error: ±0.002 Residual shear power: 4×10-10 15000 sq. deg.: n(z) ~ z2exp(-z/0.5) Photo-z rms: sz=0.04(1+z) Photo-z bias prior: sP(dz)=0.2sz Shear calibration error: ±0.003 Residual shear power: 6×10-10 KDUST宇宙学

  17. KDUST—LSST Synergy KDUST JH + LSST ugrizy 10000 sq. deg.: n(z) ~ z2exp(-z/0.6) Photo-z rms: sz=0.03(1+z) Photo-z bias prior: sP(dz)=0.2sz Shear calibration error: ±0.002 Residual shear power: 4×10-10 10000 sq. deg.: n(z) ~ z2exp(-z/0.5) Photo-z rms: sz=0.04(1+z) Photo-z bias prior: sP(dz)=0.2sz Shear calibration error: ±0.003 Residual shear power: 6×10-10 KDUST宇宙学

  18. KDUST—LSST Synergy KDUST JH + LSST ugrizy 5000 sq. deg.: n(z) ~ z2exp(-z/0.6) Photo-z rms: sz=0.03(1+z) Photo-z bias prior: sP(dz)=0.2sz Shear calibration error: ±0.002 Residual shear power: 4×10-10 15000 sq. deg.: n(z) ~ z2exp(-z/0.5) Photo-z rms: sz=0.04(1+z) Photo-z bias prior: sP(dz)=0.2sz Shear calibration error: ±0.003 Residual shear power: 6×10-10 Most importantly, KDUST helps control the systematics! SNe: SNAP like (z < 1.7) KDUST宇宙学

  19. Comparable constraints to LSST can be obtained Zhao et al. KDUST宇宙学

  20. Data: WMAP5 + small-scale CMB + SDSS LRG + ”constitution” sample (SN: CFA+UNION) New Results from Current Data Zhao & Zhang, arXiv: 0908.1568 KDUST宇宙学 20

  21. Dark Energy EOS Eigenmodes Dark energy EOS is interpolated from 30 parameters evenly spaced between a=0 and 1. KDUST modes probe slightly higher redshift than LSST ones. KDUST宇宙学

  22. Summary • Dome A has a great potential for dark energy studies. • One scenario for KDUST would be focusing on NIR (JHK) bands and obtaining ugrizy data from LSST through collaboration. • We need to explore other probes (such as strong lensing) that can take advantage of the Dome A site. • To enable the sciences that KDUST is supposed to deliver, we must study the science cases in detail now and take the data challenge very seriously. KDUST宇宙学

  23. Potential Collaborations LAMOST • Common aspects • R&D tools • Data pipelines • Data management TMT China • Transient alerts • Target selection • Precise astrometry • Precise photometry • Spectroscopic follow-up • Deep NIR imaging • High-res imaging • Redshift calibration • Survey coverage • Continuous observing KDUST宇宙学

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