Cosmology with
Photometric Redshifts
Alberto Fernández Soto
Investigador Ramón y Cajal
Observatori Astronòmic de la Universitat de València
Outline
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Spectroscopy on the verge of a nervous breakdown
Some history
Photometric redshifts 1990-2004
Applications:
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Deep space objects and counts
Galaxy formation and evolution
Cosmography
Clusters: Detection and/or confirmation
Star formation rate history
Cosmology with Gamma Ray Bursts
The Future
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Spectroscopy on the Verge
of a Nervous Breakdown
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Cosmology looks further and further away
“Interesting” objects become fainter
Signal to noise ratios fall
Biases become important
“Unmeasurable” redshifts abound
Wrong spectroscopic redshifts
Possible solution: forget spectroscopy!
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Ph-z: Some History
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First references:
– Baum (1963): 9-band photometry, cluster members
– Koo (1985): 4 bands, “poor person’s redshift machine”
– Loh & Spillar (1986): 6 bands, measure Ω
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Then “disappeared” for ~ten years...
Nowadays part of the “astronomers’ toolbox”
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Most Wanted...
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Connolly et al (1997)
Fernández-Soto et al (1999)
Sawicki et al (1997)
Lanzetta et al (1996)
Bolzonella et al (2000)
Lilly et al (1999)
Vikhlinin et al (1998)
Weymann et al (1998)
Connolly et al (1995)
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Star formation rate
Method, catalogue
Galaxy evolution
Method, catalogue
Method
Submm survey
Galaxy clusters
Galaxy at z=5.60
Method
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Ph-z: 1995-2004
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The release of the Hubble Deep Field in 1995
marks “renaissance” of ph-z techniques, offering:
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Multicolour images
Optical + Infrared information
Exquisite photometry
Major efforts in spectroscopic follow-up
Techniques: template / polynomial fitting, ANN’s
Add-ons: Bayesian priors, template evolution, new
templates, different IGM models,...
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Colours vs Redshift:
The Key to the Problem
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Given: galaxy model T, redshift z, filter+system F
Estimate the flux f(T,z,F)
Create database f(T,z,F) for:
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Type:
Redshift:
Filter:
Ell, Sab, Scd, Irr, Starburst, ...
z=0.0…18.0
Telescope + Filter responses
Given observed fluxes, search for the
‘most similar’ point in database
ÎProblem: Analyse a colour-colour diagram in
[N-1] dimensions, plus χ2 distance
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Ph-z: When?
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Photometric redshifts preferred if:
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Lots / very faint / very close objects
Accuracy can be sacrificed for numbers sake
<z> > 0.2 (to avoid cases with ∆z~z)
Only first selection is intended
Not suitable for:
– Low redshift studies
– Very high-precision work
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Ph-z: Reliability
(Fernández-Soto et al, 2001)
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Some Deep Fields
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HST (with others):
– HDF/HDFS (UBVIJHK, I=27.5, H=28.5)
– UDF (BVizJH, i=29.0, H=28.5)
– GOODS (UBVRizJHK, i=26.8, K=24.4)
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Subaru Deep Field (BVRi’z’JK, i’=26.9,K=24.4)
Gemini Deep Field (g’r’i’, r=27.2)
FORS Deep Field (UBgRIzJK, I=26.8, K=22.6)
NTT Deep Field (BVriJK, r=26.7, K=22.9)
OAC Deep Field (BVR+6 medium, R=25.1)
ALHAMBRA (20 medium+JHK, AB(visible)~25)
ÎLimits over 10m-type spectroscopic capabilities
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Applications (I):
Deep space objects and counts
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Most distant known galaxies individuated on deep
images via ph-z and “confirmed” via spectroscopy
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z=5.34 Spinrad et al 1998 (HDF+Keck)
z=5.60 Weymann et al 1998 (HDF+Keck)
z=5.78 Bunker et al 2003 (GOODS+Keck)
z~5.90 (3x) Stanway et al 2004 (GOODS+Gemini)
z=6.33 Nagao et al 2004 (Subaru)
z~10.0 Pelló et al 2004 (VLT) (*)
The only available observational estimates of the
abundance of very high-redshift galaxies are
obtained via ph-z (key for NGST, ALMA)
– Lanzetta et al (1999-HDF), Benítez et al (2003-ACS)
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Cumulative Redshift Distributions
AB(K)=24 to 28
AB(I)=24 to 28
AB(I)=24 to 28
Lanzetta et al (1999)
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Applications (II): Cosmography
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Miralles et al (1998, HDF)
Connolly et al (1998, HDF)
Magliochetti et al (1998, HDF) Arnouts et al (1999, HDF)
Arnouts et al (2002, HDFS)
Firth et al (2002, LCIR)
Brown et al (2003, NDWF) Budavari et al (2003, SDSS)
have all estimated the clustering evolution of
galaxies using ph-z catalogues. Results may be
hampered by the small size of the fields
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Phillipps et al (2000): Cosmography with elliptical
galaxies in the HDF and HDFS catalogues
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Cluster Detection and Confirmation
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Detection in optical multiband imaging surveys:
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NWDF: Juncosa & Gutiérrez de la Cruz
MUNICS: Drory et al (2003)
HIROCS: Falter et al (2004)
OACDF: Alcalá et al (2004)
Confirmation / measurement of cluster redshifts:
– BMW (Brera Multiwavelength Survey-Moretti et al
2004)
» Based on ROSAT HRI data (contains ~150 sources)
» Multiband optical/IR survey (griJHK)
» Initial results: several confirmed z~0.5-1.0 clusters
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Applications (III):
Galaxy formation and evolution
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Sawicki et al (1996, HDF)
Poli et al (1999, NTTDF)
McCarthy et al (2001, LCIR)
Bolzonella et al (2002, HDF)
Kashikawa et al (2002, SDF) Somerville et al (2003, GOODS)
Conselice et al (2004, HDF)
Gabasch et al (2004, FDF)
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Driver et al (1998), combined HDF photometric
redshifts to I=26 and morphological information to
study galaxy formation and evolution
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HDF Galaxy Sample (I)
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HDF Galaxy Sample (II)
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Applications (IV):
Star Formation Rate History
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Probably the most developed and used application
(apart from field cataloguing / object selection)
Applied to different deep fields:
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Madau 1996 (HDF)
Connolly et al 1997 (HDF)
Pascarelle et al 1998 (HDF)
Lanzetta et al 2002 (HDF+HDFS)
Rowan-Robinson (2003)
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Measurements of the SFD (1)
Madau et al. 1996
(MNRAS, 283, 1388)
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Measurements of the SFD (2)
Connolly et al. 1997
(ApJ, 486, L11)
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Measurements of the SFD (3)
Pascarelle et al. 1998
(ApJ, 508, L1)
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Measurements of the SFD (4)
Barger et al. 2000
(AJ, 119, 2092)
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Measurements of the SFD (5)
Lanzetta et al. 2002
(ApJ, 570, 492)
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Measurements of the SFD (6)
Rowan-Robinson 2003
(MNRAS, 345, 819)
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Ph-z: Use in GRB Astronomy
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Characteristics of Gamma-Ray Bursts:
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Most luminous sources in the Universe
Extremely fast evolution (minutes to hours)
Simple spectral signature in the optical (power-law)
Can be detected with small (~50cm) telescopes
Ideal for automated, robotic, Visible/NIR telescopes
REM (Zerbi et al 2001) in service in La Silla
– 60cm, Optical + NIR simultaneous cameras
– Quick, automatic photometry + photometric redshift
analysis used to trigger larger facilities (VLT)
– Aim: VLT trigger ~60 seconds after burst arrival
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The Future
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New, larger surveys (ACS, VLT+VST/VISTA)
Automatization of the method (pipelines)
Application to other environments:
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Cross-ID surveys (X-ray, radio surveys)
Integration of X-ray, FIR, radio (Aretxaga)
Further development into GRB astronomy
Slitless parallel surveys
Recovery of low-quality spectra via adaptive binning
ÎALHAMBRA (See next talk by Mariano Moles)
First large-scale galaxy survey designed ab initio
with the use of photometric redshifts in mind
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Conclusions
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Photometric redshifts are a well-established technique
Accuracy up to ∆z~0.05(1+z) for good S/N (*)
Accurate redshifts measured down to m <mLIM-2 (*)
Error estimates make them more reliable than
spectroscopic redshifts for faint sources
To be used in massive and/or very deep surveys,
first-pass selection, statistical analyses
Still needed: streamlined procedures (pipelines) and
incorporation of separate observational information
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