1. 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
11/13/2018
IoP-RAS Meeting

2. 11/13/2018
IoP-RAS Meeting

3. Outline Scientific aims of future surveys Overview of future surveys
Challenges for future surveys
Summary
11/13/2018
IoP-RAS Meeting

4. Outline Scientific aims of future surveys Overview of future surveys
Challenges for future surveys
Summary
11/13/2018
IoP-RAS Meeting

5. 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
11/13/2018
IoP-RAS Meeting

6. 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.
11/13/2018
IoP-RAS Meeting

7. Residual systematics (“B modes”)
Baryonic & Dark Matter in COSMOS
Residual systematics (“B modes”)
Blue: stellar mass
Yellow: galaxy number
Red: hot gas
B-mode map
11/13/2018
IoP-RAS Meeting
Massey, et al, Nature, 2007

8. COSMOS 3-D Dark Matter Maps
Photon equation of motion:
Right Ascension
Redshift
Declination
Cosmic Web – bottom up scenario – clusters then filaments then walls (membranes).
Note that filaments not well traced by galaxies & too ephemeral to emit x-rays, so lensing only way to detect.
11/13/2018
IoP-RAS Meeting
Massey, et al, Nature (2007)

9. 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).
r(z) z
w = 0
w = -1 d r
11/13/2018
IoP-RAS Meeting

10. 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
11/13/2018
IoP-RAS Meeting

11. 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
11/13/2018
IoP-RAS Meeting

12. Initial Conditions - Inflation
Spergel et al. ApJ, 2006
11/13/2018
IoP-RAS Meeting

13. Testing Einstein Gravity
Three tests of gravity:
Model testing. Eg, DGP, TeVeS, braneworld.
Generalized Einstein metric
Consistency Relations:
Geometric wG versus Dynamic wD.
11/13/2018
IoP-RAS Meeting

14. Outline Scientific aims of future surveys Overview of future surveys
Challenges for future surveys
Summary
11/13/2018
IoP-RAS Meeting

15. Timeline Lensing (+z) Spec IR Space 2004 SDSS/AAOmega CFHTLS DEEP2
LAMOST
Pan-STARRS UKIDSS
VST-KIDS VIKING/VHS Planck
Pan-STARRS-4
DES WFMOS JWST JWST
HSC/Subaru VIRUS
2012
SNAP/JEDI/ADEPT/Destiny?
LSST
2015
2016
DUNE DUNE DUNE
2018
2019
SKA (Radio) SKA (Radio)
ELT
11/13/2018
IoP-RAS Meeting

16. 2003-2008: CFHTLS Canada-France-Hawaii 3.6m Telescope Mauna Kea
40 CCD, 340 Mpixels
1 sq deg MegaCam
Surveys:
Wide: 170 sq deg
u*g’r’i’z’, i’=24.5
Deep: 4 sq deg, r’=28
11/13/2018
IoP-RAS Meeting

17. 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 (r=24.5)
PS4 – 4xPS1 (2009).
11/13/2018
IoP-RAS Meeting

18. 2008-2013: VST-KIDS & VIKING ESO’s Kilo-Degree Survey 2m primary
184Mpixels 1sqdeg fov OmegaCAM
1,500 sq deg
u’g’r’i’z’
VIKING
(VISTA Kilo-degree INfrared Galaxy survey)
1500 sq deg in parallel on VISTA
Z,Y,J,H,Ks
11/13/2018
IoP-RAS Meeting

19. 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
11/13/2018
IoP-RAS Meeting

20. 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? ?
11/13/2018
IoP-RAS Meeting

21. 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
11/13/2018
IoP-RAS Meeting

22. 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
AB=24.5 (10sig), zm=0.9
n0=35/sq arcmin
Ground-based optical complement
needed for photo-z’s.
11/13/2018
IoP-RAS Meeting

23. 2020-2025: SKA Square Kilometre Array (SKA) Radio interferometer.
Frequency range 100 MHz - 25 GHz
1 sq deg fov (1.4GHz) sq deg (0.7GHz)
20,000 sqdeg
zm~1.0
sz=0 (spec)
n0=10/sqarcmin
(useable HI sources)
11/13/2018
IoP-RAS Meeting

24. Grasp for optical surveys
Grasp vs. Start Date
SKA~108
103
102 10 1
LSST
HSC
PS4
DES
Grasp
(D2*fov)
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
11/13/2018
IoP-RAS Meeting
Start Date

25. Survey Area vs. End Date 104 103 102 PS1 PS4 DUNE LSST SKA HSC DES
[sqdeg]
Dark Energy Survey
Pan-STARRS-1
VST-KIDS
170 sq deg
CFHT-W
1700 sq deg
End Date
11/13/2018
IoP-RAS Meeting

26. Depth (Median Redshift)
Survey Depth vs. Area
104
103
102
PS1
DUNE
PS4/
SKA
LSST
HSC
DES
Area
[sqdeg]
VST-KIDS
Constant Time
CFHT-W
Depth (Median Redshift)
11/13/2018
IoP-RAS Meeting

27. Dark Energy Figure of Merit (FoM)
Dark Energy Task Force Figure of Merit:
Define pivot redshift, zp: wa
w(z) wp
Cosmic Web – bottom up scenario – clusters then filaments then walls (membranes).
Note that filaments not well traced by galaxies & too ephemeral to emit x-rays, so lensing only way to detect.
Dw0
w = -1 zp z
11/13/2018
IoP-RAS Meeting

28. 3-D Shear Power Background sources Dark matter halos Observer
(e.g. Heavens 2003, Kitching, Heavens & Taylor 2005)
Observer
Dark matter halos
Background sources
Cosmic Web – bottom up scenario – clusters then filaments then walls (membranes).
Note that filaments not well traced by galaxies & too ephemeral to emit x-rays, so lensing only way to detect.
11/13/2018
IoP-RAS Meeting

29. 3-D Shear Ratios
(Jain & Taylor 2003, Taylor, Kitching, Bacon, Heavens 2005)
Background sources
Dark matter halos
Observer
Cosmic Web – bottom up scenario – clusters then filaments then walls (membranes).
Note that filaments not well traced by galaxies & too ephemeral to emit x-rays, so lensing only way to detect.
Signal depends on (Wm, Wv, w0, wa) and is insensitive to clustering.
11/13/2018
IoP-RAS Meeting

30. 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
Cosmic Web – bottom up scenario – clusters then filaments then walls (membranes).
Note that filaments not well traced by galaxies & too ephemeral to emit x-rays, so lensing only way to detect.
FoM doubles every 2.5 yrs
DUNE
LSST
Saturation
11/13/2018
IoP-RAS Meeting
2023
(with Tom Kitching)
End Date

31. Outline Scientific aims of future surveys Overview of future surveys
Challenges for future surveys
Summary
11/13/2018
IoP-RAS Meeting

32. (Taylor, Kitching & Heavens, 2006)
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 db
11/13/2018
IoP-RAS Meeting x
(Taylor, Kitching & Heavens, 2006)

33. Image Distortions Image distortions: (Kitching, Taylor & Heavens 2007)
calibration rotation bias.
11/13/2018
IoP-RAS Meeting

34. 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:
11/13/2018
IoP-RAS Meeting

35. Observational Challenges
Photometric redshifts: calibration, bias, outliers
zphot
zspec
Abdalla et al (2007)
11/13/2018
(Abdalla et al, 2007; Kitching, Taylor & Heavens 2007)
IoP-RAS Meeting

36. Observational Challenges
Photometric redshifts: calibration, bias, outliers
Can estimate bias effect from Fisher analysis:
Shear Power:
Shear-ratios:
Shear-ratio:
11/13/2018
IoP-RAS Meeting

37. 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)
11/13/2018
IoP-RAS Meeting

38. Intrinsic Alignment Challenges
Two alignment effects:
Intrinsic-Intrinsic alignments
Galaxy-Intrinsic alignment
11/13/2018
IoP-RAS Meeting
(Bridle & King, 2007; Kitching, Taylor & Heavens 2007)

39. 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.
11/13/2018
IoP-RAS Meeting

40. Marginalize over Nuisance Parameters
Use data to estimate these parameters (self-calibration).
Marginalisation over uncertainties will increase error: w
Dwmarg
Dwcond
gbias
11/13/2018
IoP-RAS Meeting

41. 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 (0.015) (0.024) (0.012)
Pz+IA+g (0.03) (0.03) (0.014)
0.1% prior (0.02) (0.028) (0.012)
Mostly photo-z’s
Mostly Image Distortions
(Kitching, Taylor & Heavens 2007)
11/13/2018
IoP-RAS Meeting

42. 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
11/13/2018
IoP-RAS Meeting

43. Data Analysis Challenges
Tera/Pico-Bytes of data to push through pipeline.
(eg. LSST raw=1500GB & Cats=400GB)
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. Challinor’s Talk).
11/13/2018
IoP-RAS Meeting

44. 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.
11/13/2018
IoP-RAS Meeting

45. Conclusions Map dark matter in 3-D over all sky to z=1.
Expect DE FoM to double every 2.5 years.
Dark Energy probes saturate beyond z=1.
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!
11/13/2018
IoP-RAS Meeting