KDUST 宇宙学研讨会国台， 2009.12.16

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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.

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KDUST宇宙学

12/16/2009

Degradations due to shear errors are not bound (no self-calibration from WL itself).

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KDUST宇宙学

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Dome A site is advantageous for controlling systematic errors of cosmological probes, which is critical to the success of future surveys.

Slide from Jason Rhodes12/16/2009 8KDUST宇宙学

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Abdalla et al. (2008)

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/) 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.

Zhan et al. arXiv:0902.2599

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12/16/2009 11Zhan et al. arXiv:0902.2599

KDUST宇宙学

Slide from Tony Tyson

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Dome A LSST LSST w/ KDUST Calibration

Area/sq deg 5000—10000 20000 20000

Gal dist n(z) z2exp(-z/0.6) z2exp(-z/0.5) z2exp(-z/0.5)

Gal den/arcmin-2 70 40 40

Photo-z rms z 0.03(1+z) 0.05(1+z) 0.04(1+z)

Prior on photo-z bias P(z)

0.2z 0.3z 0.2z

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 site assumptions:n(z) ~ z2exp(-z/0.6) (peaks at z=1.2)Photo-z rms: z=0.03(1+z) (ugrizyJH)Photo-z bias prior: P(z)=0.2z

Shear calibration error: ±0.002Residual shear power: 4×10-10

Without consideration for hardware or survey

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KDUST site assumptions:n(z) ~ z2exp(-z/0.6) (peaks at z=1.2)Photo-z rms: z=0.03(1+z) (ugrizyJH)Photo-z bias prior: P(z)=0.2z

Shear calibration error: ±0.002Residual shear power: 4×10-10

LSST site assumptions:n(z) ~ z2exp(-z/0.5) (peaks at z=1)Photo-z rms: z=0.05(1+z)Photo-z bias prior: P(z)=0.3z

Shear calibration error: ±0.005Residual shear power: 10-9

Without consideration for hardware or survey

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n(z) ~ z2exp(-z/0.5)Photo-z rms: z=0.05(1+z)Photo-z bias prior: P(z)=0.3z

Shear calibration error: ±0.005Residual shear power: 10-9

LSST 20,000 sq. deg. ugrizy

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5000 sq. deg.:n(z) ~ z2exp(-z/0.6)Photo-z rms: z=0.03(1+z)Photo-z bias prior: P(z)=0.2z

Shear calibration error: ±0.002Residual shear power: 4×10-10

15000 sq. deg.:n(z) ~ z2exp(-z/0.5)Photo-z rms: z=0.04(1+z)Photo-z bias prior: P(z)=0.2z

Shear calibration error: ±0.003Residual shear power: 6×10-10

KDUST JH + LSST ugrizy

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10000 sq. deg.:n(z) ~ z2exp(-z/0.6)Photo-z rms: z=0.03(1+z)Photo-z bias prior: P(z)=0.2z

Shear calibration error: ±0.002Residual shear power: 4×10-10

10000 sq. deg.:n(z) ~ z2exp(-z/0.5)Photo-z rms: z=0.04(1+z)Photo-z bias prior: P(z)=0.2z

Shear calibration error: ±0.003Residual shear power: 6×10-10

KDUST JH + LSST ugrizy

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5000 sq. deg.:n(z) ~ z2exp(-z/0.6)Photo-z rms: z=0.03(1+z)Photo-z bias prior: P(z)=0.2z

Shear calibration error: ±0.002Residual shear power: 4×10-10

15000 sq. deg.:n(z) ~ z2exp(-z/0.5)Photo-z rms: z=0.04(1+z)Photo-z bias prior: P(z)=0.2z

Shear calibration error: ±0.003Residual shear power: 6×10-10

KDUST JH + LSST ugrizy

Most importantly, KDUST helps control the systematics!

SNe:SNAP like (z < 1.7)

Comparable constraints to LSST can be obtained

Zhao et al.

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Data: WMAP5 + small-scale CMB + SDSS LRG + ”constitution” sample (SN: CFA+UNION)

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Zhao & Zhang, arXiv: 0908.156812/16/2009 20KDUST宇宙学

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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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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

LAMOST

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•Transient alerts•Target selection•Precise astrometry•Precise photometry

TMT China

•Spectroscopic follow-up•Deep NIR imaging•High-res imaging

Common aspectsR&D toolsData pipelinesData management

•Redshift calibration•Survey coverage•Continuous observing