Photo-z for LRGs, DES, DUNE and the cross talk with Dark Energy
Ofer Lahav,University College London
1. The Dark Energy Survey
2. Photo-z methodology
3. Photo-z and probes
4. Applications: LRGs, DES, DUNE
mainly with Filipe Abdalla and Manda Banerji
Ofer LahavUniversity College London
“Evidence” for Dark Energy
Observational data• Type Ia Supernovae • Galaxy Clusters• Cosmic Microwave Background• Large Scale Structure• Gravitational Lensing
Physical effects: • Geometry • Growth of StructureBoth depend on the Hubble expansion rate:
H2(z) = H20 [M (1+z) 3 + DE (1+z) 3 (1+w) ] (flat)
Dark Energy: back to Newton?
F = -GM/r2 + /3 r
X *
“I have now explained the two principle cases of attraction…which is very remarkable”
Newton, Principia
The Future of the Local Universem =0.3
LCDMa = 1 (t= 13.5 Gyr)
OCDMa = 1 (t= 11.3 Gyr)
LCDMa = 6 (t= 42.4 Gyr)
OCDMa = 6 (t= 89.2 Gyr)
Hoffman, OL, Yepes & Dover 2007
Imaging Surveys Survey Sq. Degrees Filters Depth Dates Status
CTIO 75 1 shallow published
VIRMOS 9 1 moderate published
COSMOS 2 (space) 1 moderate complete
DLS (NOAO) 36 4 deep complete
Subaru 30? 1? deep 2005? observing
CFH Legacy 170 5 moderate 2004-2008 observing
RCS2 (CFH) 830 3 shallow 2005-2007 approvedVST/KIDS/
VISTA/VIKING 1700 4+5 moderate 2007-2010? 50%approved
DES (NOAO) 5000 4 moderate 2008-2012? proposedPan-
STARRS ~10,000? 5? moderate 2006-2012? ~funded
LSST 15,000? 5? deep 2014-2024? proposed
JDEM/SNAP1000+ (space)
9 deep 2013-2018? proposed
VST/VISTA
DUNE
5000? 2010-2015?moderate 4+5 proposed
20000? (space) 2+1? moderate 2012-2018? proposed
Y. Y. Mellier
Photo-z / Cosmology Synergy
Large Scale Structure
Clusters of Galaxies
Simulations
Photo-z
Gravitational Lensing
The Dark Energy Survey
The Dark Energy Survey • Study Dark Energy using 4 complementary techniques: I. Cluster Counts II. Weak Lensing III. Baryon Acoustic Oscillations IV. Supernovae
• Two multi-band surveys 5000 deg2 g, r, i, z 40 deg2 repeat (SNe)
• Build new 3 deg2 camera and data management system Survey 2010-2015 (525 nights)
Blanco 4-meter at CTIO
300,000,000 photometric redshifts within a volume of 23 (Gpc/h)^3, out to z = 2
DES Organization
SupernovaeB. NicholJ. Marriner
ClustersJ. MohrT. McKay
Weak LensingB. JainS. Bridle
Galaxy ClusteringE. GaztanagaW. Percival
Photometric RedshiftsF. CastanderH. Lin
SimulationsA. KravtsovA. Evrard
Science Working Groups
DES:UK consortium:
UCL, Portsmouth, Cambridge, Edinburgh, Sussex
Over 100 scientistsin 17 institutionsIn the US, UK, Spain and Brazil
DES Status
• Low-risk, near-term (2010-15) project with high discovery potential• Survey strategy delivers substantial DE science after 2 years • Synergy with SPT and VISTA • Precursor to LSST, DUNE and JDEM• Total cost is relatively modest (~ $20-30M)
STFC approved £1.7M for the DES optical corrector, subject to funding in the US
Glass ordered by UCL in Sep 07 (funds from 5 universities) DES in the US President budget request for FY08 DOE CD1 approved; CD2/CD3 in Jan 08 NSF contribution to data management
DES Forecast Constraints
•DES+Stage II combined = Factor 4.6 improvement over Stage II combined•Consistent with DETF range for Stage III DES-like project•Large uncertainties in systematics remain, but FoM is robust to uncertainties in any one probe, and we haven’t made use of all the information
DETF FoM
DES Forecasts: Power of Multiple Techniques
FoM factor 4.6 tigther compared to near term projects
w(z) =w0+wa(1–a) 68% CL
Ma, Tang, Weller
Sources of uncertaintiesin measuring Dark Energy
• Theoretical (e.g. the cosmological model)
• Astrophysical (e.g. galaxy and cluster properties)
• Instrumental (e.g. image quality)
Photometric redshifts
• Probe strong spectral features (e.g. 4000 break)
z=3.7z=0.1
Photo-z –Dark Energy cross talk
• Approximately, for a photo-z slice:
(w/ w) = 5 (z/ z) = 5 (z/z) Ns-1/2
=> the target accuracy in w
and photo-z scatter z dictate the number of required spectroscopic redshifts
Ns =105-106
Cosmology from photo-z surveys
• Optimization of Photo-z for cosmic probes
• Photo-z mocks and algorithms
• Spetroscopic training sets
• MegaZ-LRG (DR6)
• DES
• VISTA
• DUNE
• other surveys
BAO, WL, neutrino mass, ISW,
halo parameters,…
Photo-z Challenges
• Optimizing hybrid methods - errors - pdf - ‘clippping’• Optimal filters• Spetroscopic training sets • Field vs cluster photo-z• Synergy with BAO and WL• “Self calibration” and “colour tomography”
Photo-z Methods
• Template fitting (e.g. Hyper-z)
• Bayesian methods (e.g. BPZ, Zebra)
• Training-based methods (e.g. ANNz)
ANNz - Artificial Neural Network
Output:redshift
Input:magnitudes
Collister & Lahav 2004http://www.star.ucl.ac.uk/~lahav/annz.html
MegaZ-LRG *Training on ~13,000 2SLAQ
*Generating with ANNz Photo-z for ~1,000,000 LRGs
over 5,000 sq deg, 2.5 (Gpc/h)^3
z = 0.046
Collister, OL et al.
LRG - photo-z code comparison
M. Banerji, F. Abdalla F., V. Rashkov, OL et al
photo-z bins
Collister et al.
Baryon oscillations from MegaZ-LRG
Blake, Collister, Bridle & OL; astro-ph/0605303
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Halo fit to MegaZ-LRG
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Blake, Collister,OL0704.3377
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Excess Power on Large Scales?
Blake et al. 06 Padmanabhan et al. 06
The Dark Energy Survey
DES+VISTA would improve photo-z by a factor of 2 for z> 1What is the effect on WL, BAO, SNIa Science? Banerji, Abdalla, OL, Lin et al.
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DES (5 filters) vs. DES+VISTA(8 filters)
DES+VISTA: Galaxy Power Spectrum
DES grizY DES grizY + VISTA JHK
)()]([)]()[(
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11
1
*22 kPzbzDznnP
zndz
dVf
dz
dN
nPVP
mgal
sky
P
=
=
⎟⎠
⎞⎜⎝
⎛ +∝For the same clipping
threshold, we can measure the power spectrum accurately to higher redshifts using the DES+VISTA data.
DES+VISTA : Effect of Reddening
Plots generated using JPL mocks (P.Capak) which include the effects of reddening
DES z=0.8 photo-z shell
Back of the envelope: improved by sqrt (volume) => Sub-eV from DES(OL, Abdalla, Black, Kiakotou; in prep)
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DUNE: Dark UNiverse Explorer
Mission baseline: • 1.2m telescope • FOV 0.5 deg2
• PSF FWHM 0.23’’• Pixels 0.11’’ • GEO (or HEO) orbit
Surveys (3-year initial programme):• WL survey: 20,000 deg2 in 1 red broad band, 35 galaxies/amin2 with median z ~ 1, ground based complement for photo-z’s
• Near-IR survey (J,H). Deeper than possible from ground. Secures z > 1 photo-z’s
Optical and Optical+NIR
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Abdalla, Amara, Capak, Cypriano, OL , Rodes astro-ph/0705.1437
DE FoM for DUNE with and without NIR
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NIR will improve FoM by 1.3-1.7
DE FOM vs number of spectra needed
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Abdalla et al.
Photo-z Challenges
• Optimizing hybrid methods - errors - pdf - ‘clippping’• Optimal filters• Spetroscopic training sets • Field vs cluster photo-z• Synergy with BAO and WL• “Self calibration” and “colour tomography”
The END