QUaD: Measuring the Polarisation of the CMB

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Baryonic and Dark Matter
Next Generation Surveys Scientific,
Observational and Instrumental Challenges
Andy Taylor Institute for Astronomy, School of
Physics University of Edinburgh, UK
Gray Taylor et al 2005
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(No Transcript)
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Outline

• Scientific aims of future surveys
• Overview of future surveys
• Challenges for future surveys
• Summary

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Outline

• Scientific aims of future surveys
• Overview of future surveys
• Challenges for future surveys
• Summary

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Aims of Lensing Surveys

• What are the scientific challenges for lensing?
• Astrophysical
• Galaxy halo properties (galaxy-galaxy,
  galaxy-quasar)
• Clusters filaments (mass mapping vs Xray
  starlight)
• High-redshift Universe (gravitational telescopes)
• Fundamental
• Dark Matter properties ( DM mass interactions,
  neutrino mass)
• Dark Energy properties (EoS, evolution)
• Initial conditions (s8, ns, dns/dlnk)
• Testing Einstein Gravity

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Properties of Dark Matter

• Cold Dark Matter
• Mass break in matter power spectrum
• Thermal properties resolve smallest halos with
  shear and flexion.
• Neutrinos
• Mass another scale length in matter power
  spectrum from free-streaming.

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Residual systematics (B modes)
Baryonic Dark Matter in COSMOS
Blue stellar mass Yellow galaxy number Red hot
gas
B-mode map
Massey, et al, Nature, 2007
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COSMOS 3-D Dark Matter Maps
Photon equation of motion
Right Ascension
Redshift
0.0 0.2 0.4 0.6 0.8
Declination
Massey, et al, Nature (2007)
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Observable Effects of Dark Energy

• Geometry DE changes the photon distance-redshift
  relation r(z)
• Angular diameter
• 
  distance DA
• 
  Luminosity
• 
  Distance DL
• Dynamics Alters the growth of density
  perturbations, d(t).

d
r
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Constraining w from the CMB Supernova
Energy-density scales with expansion as
Close to a Cosmological Constant. (assumes flat
Universe)
Spergel et al ApJ 2006
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Constraining w from the CMB Supernova
Lensing from CFHT
Energy-density scales with expansion as
Close to a Cosmological Constant. (assumes flat
Universe)
Spergel et al ApJ 2006, Tereno et al 2006
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Initial Conditions - Inflation
Spergel et al. ApJ, 2006
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Testing Einstein Gravity

• Three tests of gravity
• Model testing. Eg, DGP, TeVeS, braneworld.
  
• Generalized Einstein metric
• Consistency Relations
• Geometric wG versus Dynamic wD.

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Outline

• Scientific aims of future surveys
• Overview of future surveys
• Challenges for future surveys
• Summary

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Timeline

• Lensing (z) Spec
  IR Space
• 2004
  SDSS/AAOmega
• CFHTLS DEEP2
• 2006 LAMOST
• Pan-STARRS-1
  UKIDSS
• VST-KIDS VIKING/VHS
  Planck
• 2009 Pan-STARRS-4
• DES WFMOS JWST
  JWST
• 2011 HSC/Subaru VIRUS
• 2012
• 2013 SNAP/JEDI/ADEPT/Destiny?
• 2014 LSST
• 2015
• 2016
• DUNE DUNE DUNE
• 2018
• 2019
• SKA (Radio) SKA (Radio)
• 2025 ELT

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2003-2008 CFHTLS

• Canada-France-Hawaii 3.6m Telescope
• Mauna Kea
• 40 CCD, 340 Mpixels
• 1 sq deg MegaCam
• Surveys
• Wide 170 sq deg
• ugriz, i24.5
• Deep 4 sq deg, r28

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2007-2010 Pan-STARRS-1

• Panoramic Survey Telescope and Rapid
• Response System (Pan-STARRS).
• Hawaii, MPIA, Taiwan,
• Harvard, Johns Hopkins,
• UK (Edinburgh, Belfast, Durham),
• 1.8 meter primary
• 1.4Gpixel camera.
• 7 sq deg fov.
• Medium Deep Survey
• 3p Survey
• g, r, i, z, y (r24.5)
• PS4 4xPS1 (2009).

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2008-2013 VST-KIDS VIKING

• ESOs Kilo-Degree Survey
• 2m primary
• 184Mpixels 1sqdeg fov OmegaCAM
• 1,500 sq deg
• ugriz
• VIKING
• (VISTA Kilo-degree INfrared Galaxy survey)
• 1500 sq deg in parallel on VISTA
• Z,Y,J,H,Ks

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2010-2015 DES

• The Dark Energy Survey.
• 4-metre Blanco at CTIO (South)
• 500 Megapixel, 3 sqdeg fov camera
• 5 yr survey (30 of time).
• g,r,i,z over 5000 sq deg
• r 24.1 (10sig)
• 4 dark energy probes
• WL, BAOs, SN Clusters

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2011-2016 Subaru-HyperSuprimeCam

• 8.3m Primary
• 3.14 sq deg fov
• 1.4 Gpixel camera
• HyperSuprimeCam
• 3500 sq deg/year
• 17,500 sq deg (5 yrs)
• ugriz?

?
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2014-2024 LSST

• Large Synoptic Survey Telescope (LSST)
• 8.4m (effectively 6.5m) Primary
• 3.2 Gpixel, 9.6 sq deg fov camera
• ugrizY
• Cerro Pachon, Chile
• 30Tbyte per night

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2017-2021 DUNE Dark UNiverse Explorer

• Proposal to ESA Cosmic Visions programme.
• 1.2m satellite telescope
• r-i-z Y,J,H
• 0.5 sq deg fov
• 3-year weak lensing survey
• 20,000 sq deg
• AB24.5 (10sig), zm0.9
• n035/sq arcmin
• Ground-based optical complement
• needed for photo-zs.

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2020-2025 SKA

• Square Kilometre Array (SKA)
• Radio interferometer.
• Frequency range 100 MHz - 25 GHz
• 1 sq deg fov (1.4GHz) - 200 sq deg (0.7GHz)
• 20,000 sqdeg
• zm1.0
• sz0 (spec)
• n010/sqarcmin
• (useable HI sources)

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Grasp vs. Start Date
SKA108
103 102 10 1
LSST
HSC
PS4
DES
Grasp (D2fov)
Dark Energy Survey
Pan-STARRS-1
PS1
Grasp for optical surveys doubles every 2.5 yrs
CFHT
VST-KIDS
170 sq deg
DUNE
1700 sq deg
2002 2004 2006 2008 2010 2012
2014 2016 2018 2020
Start Date
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Survey Area vs. End Date
104 103 102
PS1
PS4
SKA
LSST
DUNE
HSC
DES
Area sqdeg
Dark Energy Survey
Pan-STARRS-1
VST-KIDS
170 sq deg
CFHT-W
1700 sq deg
2008 2010 2012 2014 2016
2018 2020 2022 2024 2026
End Date
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Survey Depth vs. Area
104 103 102
PS1
DUNE
PS4/ SKA
LSST
HSC
DES
Area sqdeg
VST-KIDS
Constant Time
CFHT-W
0.6 0.7 0.8 0.9 1.0
1.1 1.2 1.3
Depth (Median Redshift)
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Dark Energy Figure of Merit (FoM)

• Dark Energy Task Force Figure of Merit
• Define pivot redshift, zp

wa
w(z)
wp
Dw0
w -1
zp
z
0
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3-D Shear Power
(e.g. Heavens 2003, Kitching, Heavens Taylor
2005)
Background sources
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3-D Shear Ratios

• (Jain Taylor 2003, Taylor, Kitching, Bacon,
  Heavens 2005)

Background sources
Dark matter halos
Observer

• Signal depends on (Wm, Wv, w0, wa) and is
  insensitive to clustering.

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FoM for Dark Energy from Lensing
3-D shear power and shear-ratios combined with
Planck Explorer CMB survey (2008)
Current limit
1/FoM
CFHT
KIDS
PS1
SKA
DES
HSC
PS4
FoM doubles every 2.5 yrs
DUNE
LSST
Saturation
2023
End Date
(with Tom Kitching)
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Outline

• Scientific aims of future surveys
• Overview of future surveys
• Challenges for future surveys
• Summary

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Effect of Systematics

• What is the effect of systematics in results?
• Can estimate effect using Fisher Matrix
  formalism
• Eg for a straight line zero-point fit

y
b
x
(Taylor, Kitching Heavens, 2006)
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Image Distortions

• Image distortions (Kitching, Taylor
  Heavens 2007)
• calibration rotation bias.

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Image Distortions

• Image distortions calibration, rotation, bias.
• Effect of these on constant w
• Shear Power no 1st order effect from gbias
• Shear-ratios No bias to 1st order.
• Require

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Observational Challenges

• Photometric redshifts calibration, bias,
  outliers

zphot
zspec
Abdalla et al (2007)
(Abdalla et al, 2007 Kitching, Taylor Heavens
2007)
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Observational Challenges

• Photometric redshifts calibration, bias,
  outliers
• Can estimate bias effect from Fisher analysis
• Shear Power
• Shear-ratios

Shear-ratio
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Photometric Redshift Challenges

• 5-optical 3-IR? VST-KIDS/VIKING.
• Do we need U-band? VST-KIDS
• Calibration with spectroscopic surveys
• How many? 105? VLT, WFMOS, ELTs?
• Need synergy with IR spectroscopic surveys.

Abdalla et al (2007)
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Intrinsic Alignment Challenges

• Two alignment effects
• Intrinsic-Intrinsic alignments
• Galaxy-Intrinsic alignment

(Bridle King, 2007 Kitching, Taylor Heavens
2007)
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Intrinsic Alignment Challenges

• Model using Heymans et al (2006).
• Find no effect on shear-ratio signal (averaged
  out),
• but enters noise.
• Minimal effect on shear-power (but see Bridle
  King 2007).
• Using signal where alignment contribution is
  small.

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Marginalize over Nuisance Parameters

• Use data to estimate these parameters
  (self-calibration).
• Marginalisation over uncertainties will increase
  error

w
Dwmarg
Dwcond
gbias
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Marginalize over Nuisance Parameters

• Effect of marginalisation over image distortion
  uncertainties, for Shear Ratios Planck
• For a DUNE mission FoM (Dwp)
• Shear Power Shear-Ratio
  Combined
• Baseline 500 (0.015) 150 (0.024)
  915 (0.012)
• PzIAg 116 (0.03) 70 (0.03)
  670 (0.014)
• 0.1 prior 440 (0.02) 100 (0.028)
  900 (0.012)

Mostly photo-zs
Mostly Image Distortions
(Kitching, Taylor Heavens 2007)
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Nonlinear Matter Distribution

• Non-linear matter power spectrum.
• Fitting functions not accurate
• Need MC N-body sims
• Baryons?
• Non-Gaussian corrections to the shear field.
• Covariance of power (4pt-fn)
• Higher-order correlations
• Non-Gaussian likelihoods

log Clgg
log l
P(k)
k
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Data Analysis Challenges

• Tera/Pico-Bytes of data to push through pipeline.
• (eg. LSST raw1500GB Cats400GB)
• 4 layers
• Data acquisition, book keeping
• Raw Data Reduction (registration)
• Shape analysis (KSB, shapelets, K2K, automated)
• Science analysis (map making, power spectra, etc)
• How do we simulate large dynamic range?
• And Monte-Carlo surveys 1000 times?
• c.f. CMB temperature polarisation experiments
• (see A. Challinors Talk).

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Organizational Challenges

• How to coordinate the effort?
• EU Research-Training Network.
• DUEL (Dark Universe with Extragalactic Lensing)
• Exploit Cosmological Lensing from
• CFHTLS, Pan-STARRS, VST-KIDS
• Plan for future surveys (DUNE)
• 8 Network Partners
• Edinburgh, Paris, Bonn, Heidelberg, Munich,
  Leiden, Naples, British Columbia
• 7 Postdocs 7 PhD students across network.
• Training exchange of methods data.

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Conclusions

• Map dark matter in 3-D over all sky to z1.
• Expect DE FoM to double every 2.5 years.
• Dark Energy probes saturate beyond z1.
• Bias in nuisance parameters biases w.
• Self-calibration leads to doubling of errors.
• Can add extra priors, or combine WL methods.
• Optical/IR, Photo-z/Spec-z, Ground-Space
  synergies
• Major challenges in data analysis lie ahead!