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，

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?
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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 ~
Our forecasts for future surveys assume <m> ~ 0.5% and <c> ~ 10-5.
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KDUST宇宙学

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

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

8. Dome A Advantages
Slide from Jason Rhodes
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9. Simulation of Residual Shear
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10. Photo-z Sys Effects on DE Constraints
Abdalla et al. (2008)
Zhan et al. arXiv:
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.
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KDUST宇宙学

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

12. LSST w/ KDUST Calibration
KDUST—LSST Synergy
Dome A
LSST
LSST w/ KDUST Calibration
Area/sq deg
5000—10000
20000
Gal dist n(z)
z2exp(-z/0.6)
z2exp(-z/0.5)
Gal den/arcmin-2 70 40
Photo-z rms sz
0.03(1+z)
0.05(1+z)
0.04(1+z)
Prior on photo-z bias sP(dz)
0.2sz
0.3sz
Shear calibration error (×)
±0.002
±0.005
±0.003
Residual shear power (+)
4x10-10
10-9
6x10-10
SNeIa zmax
>~ 2
0.8/1.2 --
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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
12/16/2009
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
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KDUST宇宙学

15. KDUST—LSST Synergy n(z) ~ z2exp(-z/0.5) Photo-z rms: sz=0.05(1+z)
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
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KDUST宇宙学

16. KDUST—LSST Synergy 5000 sq. deg.: n(z) ~ z2exp(-z/0.6)
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
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KDUST宇宙学

17. KDUST—LSST Synergy 10000 sq. deg.: n(z) ~ z2exp(-z/0.6)
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
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KDUST宇宙学

18. Most importantly, KDUST helps control the systematics!
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)
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KDUST宇宙学

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

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

23. Potential Collaborations
Common aspects
R&D tools
Data pipelines
Data management
LAMOST
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
12/16/2009
KDUST宇宙学