Wide Field Optical Surveys

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Wide Field Optical Surveys

• Naoki Yasuda (U.Tokyo, Japan)

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Optical Sky

• Optical view of the sky provides us basic
  knowledge of our universe.
• There is a long history for optical observation
  of the sky.
• Schmidt Surveys
• Sloan Digital Sky Survey
• Time-Domain Surveys

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Before CCDs are used
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Schmidt Survey

• Wide Field Schmidt Telescope
• 6 x 6 square degree FOV
• 1200 plates to cover whole sky
• 15 90 minutes exposure time
• Takes long time to complete

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Palomar Observatory Sky Survey

• POSS-I (1950-57)
• Cover the sky of d gt -30
• Blue (400nm) and Red (650nm)
• 936 photographic plates
• POSS-II (1987-1999)
• Finer grain and fast emulsions
• 103aO -gt IIIaJ, 103aE -gt IIIaF
• Install achromatic corrector
• 897 plates

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Southern Surveys

• UK Schmidt
• SERC(J) (1974-87) 606 plates
• AAO(R) (1989-) 606 plates
• ESO Schmidt
• ESO(B) (1973-78)
• ESO(R) (1973-88)

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Catalogs

• Plates were subject to Eye inspection by many
  astronomers.
• Many basic catalogs were published (galaxies)
• Morphological Catalogue of Galaxies
• Vorontsov-Velyaminov et al. (1962-68)
• Catalogue of Galaxies and Clusters of Galaxies
• Zwicky et al. (1961-68)
• Uppsala General Catalogue of Galaxies
• P. Nilson (1973)
• ESO/Uppsala Survey of the ESO(B) Atlas
• A. Lauberts (1982)
• Southern Galaxy Catalogue
• Corwin (1985)
• Sample selections and measurements are not
  accurate

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Digitization

• Photodensitometer
• Measure density of plates
• Need long time to scan full plate
• From plates to digital information
• COSMOS / APM
• Automated measuring machines
• Advantage of digital data
• Easy to use
• Automated analysis
• Available on-line

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Information Technology

• Plates/film ? Electronic devices (CCD)
• Plate archive ? Digital archive
• Eye scanning ? Automated analysis
• IT has changed the way of astronomy
• Quality assurance
• Uniform data acquisition
• Easy usability of huge dataset

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Sloan Digital Sky Survey
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Sloan Digital Sky Survey

• First CCD based wide-field photometric /
  spectroscopic survey
• Dedicated wide-field telescope
• Mosaic CCD Camera
• Multi-fiber spectrograph
• Dedicated data reduction pipeline
• Quick reduction for 5MB/sec data rate
• Dedicated science database
• All the data are stored in database and accessible

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Objective of SDSS

• Mapping the sky
• Explore our nearby universe (z lt 0.2)
• Imaging the quarter of whole sky
• Spectroscopy of 1 million galaxies
• Generate high-quality and uniform dataset
• Well calibrated flux
• Enable statistical study using large dataset

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Initial Goals

• Imaging 10,000 square degree of northern sky down
  to 23mag in 5 optical band (3900-9200A)
• 7 x 107 stars
• 5 x 107 galaxies
• 1 x 106 quasars
• Measure redshifts for 106 galaxies and 105
  quasars
• Create largest, homogeneous, and high-quality
  catalog of galaxies and quasars

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Telescope and Camera
FOV is 3 degree in diameterPixel size is
0.4arcsec/pixel
Direction of objects moving
2.5m main telescope
Photometric telescope
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Filter System
AB system based on 4 primary standards
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Spectrograph
640 Fiber Plug Plate
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Time Delay and Integrate

• Called as drift scan
• Synchronize the read-out rate of CCD and objects
  movements due to earth rotation
• Continuously scan the sky
• No loss for pointing, shutter, and read-out
• Exposure time is 54 sec
• Long strips will be observed

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Survey Stripe
2.5 degree
6 long strips will be observed for each run of
observation. Next day, slightly change the
pointing to fill the gap. With these two
observation, one stripe with width of 2.5 degree
will be complete 45 stripes will be observed.
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Sky Region of Observation
Observe around north Galactic pole to avoid
Galactic extinction
Map of Galactic extinction
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Observed area (cumulative)
Spectroscopy
Imaging
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Imaging
Spectroscopy
Gap
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End of SDSS-I

• SDSS-I has finished on June 2006
• Imaging 8900 deg2 (unique NS)
  detected 210 million objects
  7800 deg2 (footprint NS)
  7300 deg2 (footprint N)
• Spectroscopy 1880 plates
  1150 N 5800 deg2
  160 S 570
  special 1.14 million spectra of
  celestial objects

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Survey Plates

• End of SDSS-I
• 1310 plates 6300 deg2
• 840,000 spectra total
• 590,000 galaxies
• 75,000 quasars
• 110,000 stars
• Diameter 32 degree
• Radius 267Mpc
• Volume 0.02 Gpc3

• End of LEGACY
• 1700 plates 8200 deg2
• 1,090,000 spectra
• 760,000 galaxies
• 98,000 quasars
• 140,000 stars
• Diameter 70 degree
• Radius 450Mpc
• Volume 0.09 Gpc3

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SDSS Data Release
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Catalogs in Database

• Imaging
• Magnitude (5 bands) ? 4 colors
• PSF, Fiber, Petrosian, and model magnitudes
• How to discriminate these different kind of
  magnitudes in VO? Does UCD have enough
  functionality?
• Size, shape, position
• Radial profiles etc
• Spectroscopy
• Object type
• Redshift
• Velocity dispersion
• Line strength

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data Sloan Digital Sky Survey and the Bright
Star Catalog
visualization David W. Hogg (NYU) with help from
Blanton, Finkbeiner, Padmanabhan, Schlegel, Wherry
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Results of Redshift Survey
Pie Diagram Distribution of 66,976 galaxies that
lie near celestial equator. The color
representing the luminosity of galaxies.
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Scientific results from SDSS

• There are so many results.
• I cannot talk about them here.
• Cosmology Power spectrum, Clustering, BAO,
• Galaxies Statistical properties, Evolution,
• Quasars Statistical properties, High-z QSOs,
  Gravitational lenes,
• Stars L/T dwarfs, White dwarfs, Galactic
  structure,
• Characteristics are natural consequences of large
  survey
• Homogeneous and large sample ? statistical study
• Serendipitous objects

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Cosmological Perturbations
LCDMadjusted to L galaxy
Wm 0.28, h 0.72 Wb/Wm 0.16, t 0.17
P(k)
Tegmark et al. (2004)
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SDSS Power Spectrum

• Tegmark et al. (2004) has calculated P(k) at
  large-scale using SDSS main sample.
• Good agreement with CDM model.
• Constrain cosmological parameters using the
  result of CMB and SNe.

Scale ofcluster ofgalaxies
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WMAP and SDSS are complimentary for constraining
cosmological parameters
WMAP
WMAPSDSS
Tegmark et al. (2004)
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Constraints on dark energy
Seljak et al. (2005)
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LRG Power Spectrum
LRG Small errors due to largevolume
Main sample
Eisenstein et al. (2004)
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Acoustic Oscillations in the CMB
WMAP team (Bennett et al. 2003)
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Acoustic peak in SDSS
Single peak incorrelation function
No peak in pureCDM model
Eisenstein et al. (2004)
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Two point correlation function

• Deviation from power-law over 1Mpc.
• Correlation within dark matter halo and
  correlation between dark matter halo.

Zehavi et al. (2004)
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Galaxy Luminosity Function

• Detailed luminosity functions of local galaxies
• Dependence of galaxy properties and environments
• field
• cluster
• void
• Constraint on galaxy evolution

Blanton et al. (2005)
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Bimodality in Galaxy Properties

• Bimodality in color, luminosity profile, and
  luminosity
• M3x1010MoVc120km/sm3x108Mo/kpc2are
  threshold
• Heavy galaxies are old and light galaxies are
  young.? opposite to clustering

Blanton et al. 2002
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Mass-Metallicity relation

• Little metal in small mass galaxies.
• Galactic wind of SNe plays important role.
• This is effective to 1010Mo.

Tremonti et al. (2004)
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Halo stars around disk galaxies

• Composite of many images

Zibetti et al. (2004)
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Cosmic Magnification

• Magnification of QSO due to gravitational lensing
  effect.
• Correlation function between QSO and galaxies is
  a probe.
• Magnification of QSO
• Decrease of number density of QSO
• 4s detection

Scranton et al. (2005)
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High-z QSOs

• Detected as very red objects (i-dropout).
• 12 QSOs at 5.7ltzlt6.4 has been found.

Fan et al. (2003)
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Gunn-Peterson Trough
Becker et al. (2001)
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White et al. (2003)
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L dwarfs, T dwarfs
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Data Archives

• Main page
• http//www.sdss.org/dr5/index.html
• Describe data products, instruments, and
  algorisms
• Data Archive Server
• http//das.sdss.org/DR5-cgi-bin/DAS
• Serve flat files (FITS format)
• Catalog Archive Server
• http//cas.sdss.org/astrodr5/en/
• Search tools for SDSS catalogs
• Casjobs
• http//casjobs.sdss.org/casjobs/
• Batch job server for SQL searches

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CAS
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Complicated DB queries are possible via SQL
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Color images (g, r, i) of all area can be
retrieved
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CASJobs

• Batch Query Services
• SQL access to the SDSS database
• Execute queries background
• When finished, e-mail will be send
• Local storage MyDB
• Store query results
• Import users own tables
• Extract to FITS, VOTABLE, or CSV
• Join tables with tables in any SDSS DB
• Publish tables to groups

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Dataset for VO

• Quality Assurance
• Homogeneous sampling
• Same telescope, same software
• Well calibrated catalog
• Photometric telescope
• Ready to do science

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Impact on VO

• Pre-maid catalogs are extensively used.
• Casjobs, OpenSkyQuery,
• How about images?
• Measuring our own parameters,
• We can download images and measure them at our
  own site.
• But this is not VO like.
• Image processing in VO require framework to run
  user own programs and huge storage, high-speed
  cpu, and high-speed network.

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Reference Optical Sky

• POSS-I over 50 years
• SDSS has becoming a reference of local universe
  including spectroscopic data.
• More than 1,000 papers referring SDSS
• 10-15 years
• SDSS is not covering entire sky
• Only 20 25
• Next large surveys like LSST are planned

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Time Domain Survey
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Time Domain Survey

• Add another axis
• Spatial, redshift, wavelength time
• Variability
• Supernovae, variable stars, AGNs,
• Proper Motion
• Nearby faint stars, moving objects,

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SDSS-II Supernova Survey

• One of SDSS-II projects (Legacy, SEGUE)
• Using the SDSS 2.5m telescope
• During September November of 2005-2007
• To scan 300 square degrees of the sky every 2
  days
• Discover supernovae and obtain multi-color light
  curves
• Spectroscopic follow-up by ARC, HET, MDM, WHT,
  Subaru

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Observation Area
N S
Decl.1.25d
2.5deg
Decl.-1.25d
R.A.20
R.A.4h
120deg
North and south stripes are observed every 2 days
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Science goals

• Type Ia supernovae (SNe)
• Spectroscopically confirm and obtain
  well-measured light curves of 200 SN Ia from z
  0.05 0.4
• Bridge low-z (zlt0.05 LOSS, SNF) and high-z
  (0.3ltzlt1.0 ESSENCE, SNLS) sources
• Understand and minimize systematics of SN Ia as
  distance indicators
• SN Ib/c, II, rare types
• Other transients

Redshift desert
Astier et al. (2006)
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Survey Procedures

• Image subtraction
• Search for variable objects using old data as
  reference
• Process the data of 1 night within 20 hours
• Scan by Eyes
• Reduce candidates to 1/10
• Light curve fit
• Make template light curves from multi-epoch
  spectra
• Fit for redshift, extinction, stretch for Ia
• Able to type with gt90 efficiency after 2-4
  epochs
• Selection of spectroscopic targets

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2005 Run Summary

• Conditions ranged from SDSS survey quality
  (photometric, dark, good seeing) to mixed clouds
  and moon.

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Results from Fall 2005

• 130 spectroscopically confirmed SN Ia
• 14 spectroscopically probable SN Ia
• 6 SN Ib/c (3 hypernovae)
• 11 SN II (4 type IIn)
• 5 AGN
• hundreds of other unconfirmed SNe with good
  light curves (galaxy spectroscopic redshifts
  measured for 25 additional Ia candidates)
• Focused primarily on Ia

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2005 spectroscopically confirmed
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Plans for Fall 2006 Survey

• Upgrade search algorithm to reduce scan load
• Real time alert webpage (possibly VOEvent)
• Find and actively follow up other types of SNe
  II-P, Ibc and hypernovae, Ia-pec
• Densely-sampled multi-epoch spectroscopy of
  selected nearby targets
• Spectral sequence, systematics, rare types

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Data Archive

• Corrected imaging frames and catalog of imaging
  objects are available at http//das.sdss.org/DRSN1
  -cgi-bin/DASsn
• This is SDSS DAS interface
• Not yet loaded into databases

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Subaru Deep Survey

• Subaru Deep Field (SDF)spring, 1 Suprime-Cam FOV
• UV (GALEX)
• NIR (UKIRT/WFCAM)
• MIR (Spitzer)
• Subaru/XMM-Newton Deep Fieldfall, 5 Suprime-Cam
  FOVs
• X-ray (XMM-Newton)
• NIR (UKIRT/WFCAM)
• MIR (Spitzer)
• (sub-mm (JCMT/SCUBA))
• Radio (VLA)

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Optically Variable Object Surveys

• From 2001 to 2006
• Time intervals 1hr-5yrs
• 27x34x6 FOVs
• 1 SDF
• 5 SXDF
• 9-16 epochs
• 1 hr exposure
• i-band
• i26mag_at_AB

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Detected Objects

• Find variable objects in subtraction images
• 400-500 objects / Suprime-Cam FOV? 2,000
  objects / deg2
• Longer time intervals, more variable objects.

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An example of variable objects
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Object Classification

• Classify objects into 3 categories
• Variable stars
• Supernovae (SNe)
• AGN
• Based on their
• Light curves SNe or not
• Offsets of positions from host objects SNe or
  not
• Morphology of host objects stars or not
• Colors of host objects stars or not

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Object Classification

• Of 1,220 objects in 3 FOVs in SXDF
• Variable stars 154 (12)
• Supernovae 594 (49)roughly consistent with
  predicted number from SN rates
• AGN 472 (39)Optical variability plays a
  complementary role with X-ray data

Some of blue stars in this region may be
quasars misclassified as stars because of
similar colors
Variable stars
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Optical magnitude X-ray flux

• Of gt1,000 X-ray sources, only 200 objects are
  optically variable.
• On the other hand, 200 AGN candidates are not
  detected in X-rays

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High Proper Motion Objects

• 17 high proper motion objects are detected from
  SDF 4 year data
• gt0.034 arcsec / year
• 12 objects are candidates of WDs

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Summary

• SDSS has becoming reference opitical sky
• Object catalogs are heavily used
• Use of images may require more sophisticated VO
  systems
• Time domain survey will give us new information
• Deep time domain survey looks interesting
• Various time intervals are important
• Proper motion also require long time interval