Post on 26-Dec-2015
Alex SzalayDepartment of Physics and Astronomy
The Johns Hopkins University
The Sloan Digital Sky Survey
The Sloan Digital Sky Survey
Alex Szalay, JHU
A project run by the Astrophysical Research Consortium (ARC)A project run by the Astrophysical Research Consortium (ARC)
Goal: To create a detailed multicolor map of the Northern Skyover 5 years, with a budget of approximately $80M
Data Size: 40 TB raw, 1 TB processed
Goal: To create a detailed multicolor map of the Northern Skyover 5 years, with a budget of approximately $80M
Data Size: 40 TB raw, 1 TB processed
The University of Chicago Princeton University The Johns Hopkins University The University of Washington Fermi National Accelerator Laboratory US Naval Observatory The Japanese Participation Group The Institute for Advanced Study
SLOAN Foundation, NSF, DOE, NASA
The University of Chicago Princeton University The Johns Hopkins University The University of Washington Fermi National Accelerator Laboratory US Naval Observatory The Japanese Participation Group The Institute for Advanced Study
SLOAN Foundation, NSF, DOE, NASA
The Sloan Digital Sky SurveyThe Sloan Digital Sky SurveyThe Sloan Digital Sky SurveyThe Sloan Digital Sky Survey
Alex Szalay, JHU
Scientific MotivationScientific MotivationScientific MotivationScientific Motivation
Create the ultimate map of the Universe: The Cosmic Genome Project!
Study the distribution of galaxies: What is the origin of fluctuations?
What is the topology of the distribution?
Measure the global properties of the Universe: How much dark matter is there?
Local census of the galaxy population: How did galaxies form?
Find the most distant objects in the Universe: What are the highest quasar redshifts?
Alex Szalay, JHU
Cosmology PrimerCosmology PrimerCosmology PrimerCosmology Primer
The spatial distribution of galaxies is correlated, due to small ripples in the early Universe
P(k):P(k): power spectrumP(k):P(k): power spectrum
v = Hv = Hoo r rHubble’s law
v = Hv = Hoo r rHubble’s law
The Universe is expanding: the galaxies move away from us spectral lines are redshifted
= = density/criticaldensity/criticalif <1, expand forever
= = density/criticaldensity/criticalif <1, expand forever
The fate of the universe depends on the balance between gravity and the expansion velocity
dd> > **dd> > **
Most of the mass in the Universe is dark matter, and it may be cold (CDM)
Alex Szalay, JHU
The ‘Naught’ ProblemThe ‘Naught’ ProblemThe ‘Naught’ ProblemThe ‘Naught’ Problem
What are the global parameters of the Universe?
H0 the Hubble constant 55-75 km/s/Mpc0 the density parameter 0.25-10 the cosmological constant 0 - 0.7
Their values are still quite uncertain today...
Goal: measure these parameters with an accuracy of a few percent
What are the global parameters of the Universe?
H0 the Hubble constant 55-75 km/s/Mpc0 the density parameter 0.25-10 the cosmological constant 0 - 0.7
Their values are still quite uncertain today...
Goal: measure these parameters with an accuracy of a few percent
High Precision Cosmology!High Precision Cosmology!High Precision Cosmology!High Precision Cosmology!
Alex Szalay, JHU
The Cosmic Genome ProjectThe Cosmic Genome ProjectThe Cosmic Genome ProjectThe Cosmic Genome Project
The SDSS will create the ultimate mapof the Universe, with much more detailthan any other measurement before
Gregory and Thompson 1978
deLapparent, Geller and Huchra 1986daCosta etal 1995
SDSS Collaboration 2002
Alex Szalay, JHU
Area and Size of Redshift SurveysArea and Size of Redshift SurveysArea and Size of Redshift SurveysArea and Size of Redshift Surveys
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
1.00E+08
1.00E+09
1.00E+04 1.00E+05 1.00E+06 1.00E+07 1.00E+08 1.00E+09 1.00E+10 1.00E+11
Volume in Mpc 3
No
of
ob
jec
ts
LCRS
SDSSmain
SDSSred
SDSSabs line
SDSSphoto-z
2dFRCfA+SSRS
SAPMQDOT
2dF
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
1.00E+08
1.00E+09
1.00E+04 1.00E+05 1.00E+06 1.00E+07 1.00E+08 1.00E+09 1.00E+10 1.00E+11
Volume in Mpc 3
No
of
ob
jec
ts
LCRS
SDSSmain
SDSSred
SDSSabs line
SDSSphoto-z
2dFRCfA+SSRS
SAPMQDOT
2dF
Alex Szalay, JHU
Clustering of GalaxiesClustering of GalaxiesClustering of GalaxiesClustering of Galaxies
We will measure the spectrum of the density fluctuations to high precision even on very large scales
The error in the amplitude of the fluctuation spectrum
1970 x1001990 x21995 ±0.41998 ±0.21999 ±0.12002 ±0.05
The error in the amplitude of the fluctuation spectrum
1970 x1001990 x21995 ±0.41998 ±0.21999 ±0.12002 ±0.05
Alex Szalay, JHU
Relevant ScalesRelevant ScalesRelevant ScalesRelevant Scales
Distances measured in Mpc [megaparsec] 1 Mpc = 3 x 1024 cm 5 Mpc = distance between galaxies3000 Mpc = scale of the Universe
if >200 Mpcfluctuations have a PRIMORDIAL shape
if <100 Mpcgravity creates sharp features, like walls,filaments and voids
Biasingconversion of mass into light is nonlinearlight is much more clumpy than the mass
Alex Szalay, JHU
The Topology of Local UniverseThe Topology of Local UniverseThe Topology of Local UniverseThe Topology of Local Universe
Measure the Topology of the Universe Does it consist of walls and voids or is it randomly distributed?
Alex Szalay, JHU
Finding the Most Distant ObjectsFinding the Most Distant ObjectsFinding the Most Distant ObjectsFinding the Most Distant Objects
Intermediate and high redshift QSOs Multicolor selection function. Luminosity functions and spatial clustering. High redshift QSO’s (z>5).
Intermediate and high redshift QSOs Multicolor selection function. Luminosity functions and spatial clustering. High redshift QSO’s (z>5).
Alex Szalay, JHU
Special 2.5m telescope, located at Apache Point, NM3 degree field of view.Zero distortion focal plane.
Two surveys in one:Photometric survey in 5 bands.Spectroscopic redshift survey.
Huge CCD Mosaic30 CCDs 2K x 2K (imaging)22 CCDs 2K x 400 (astrometry)
Two high resolution spectrographs2 x 320 fibers, with 3 arcsec diameter.R=2000 resolution with 4096 pixels.Spectral coverage from 3900Å to 9200Å.
Automated data reductionOver 70 man-years of development effort.(Fermilab + collaboration scientists)
Very high data volumeExpect over 20 TB of raw data.About 1 TB processed catalogs.Data made available to the public.
Features of the SDSSFeatures of the SDSSFeatures of the SDSSFeatures of the SDSS
Alex Szalay, JHU
Apache Point ObservatoryApache Point ObservatoryApache Point ObservatoryApache Point Observatory
Located in New Mexico,near White Sands National Monument
Located in New Mexico,near White Sands National Monument
Alex Szalay, JHU
The TelescopeThe TelescopeThe TelescopeThe Telescope
Special 2.5m telescope 3 degree field of view Zero distortion focal plane Wind screen moved separately
Special 2.5m telescope 3 degree field of view Zero distortion focal plane Wind screen moved separately
Alex Szalay, JHU
Northern Galactic Cap 5 broad-band filters ( u', g', r', i', z’ ) limiting magnitudes (22.3, 23.3, 23.1, 22.3, 20.8) drift scan of 10,000 square degrees 55 sec exposure time 40 TB raw imaging data -> pipeline ->
100,000,000 galaxies 50,000,000 stars
calibration to 2% at r'=19.8 only done in the best seeing (20 nights/yr) pixel size is 0.4 arcsec, astrometric precision is 60 milliarcsec
Southern Galactic Cap multiple scans (> 30 times) of the same stripe
Continuous data rate of 8 Mbytes/sec
The Photometric SurveyThe Photometric SurveyThe Photometric SurveyThe Photometric Survey
Alex Szalay, JHU
The Footprint of the SurveyThe Footprint of the SurveyThe Footprint of the SurveyThe Footprint of the Survey
Alex Szalay, JHU
Survey StrategySurvey StrategySurvey StrategySurvey Strategy
Overlapping 2.5 degree wide stripes
Avoiding the Galactic Plane (dust)
Multiple exposures on the three Southern stripes
Overlapping 2.5 degree wide stripes
Avoiding the Galactic Plane (dust)
Multiple exposures on the three Southern stripes
Alex Szalay, JHU
Measure redshifts of objects distance
SDSS Redshift Survey:1 million galaxies100,000 quasars100,000 stars
Two high throughput spectrographsspectral range 3900-9200 Å.640 spectra simultaneously.R=2000 resolution.
Automated reduction of spectraVery high sampling density and completenessObjects in other catalogs also targeted
The Spectroscopic SurveyThe Spectroscopic SurveyThe Spectroscopic SurveyThe Spectroscopic Survey
Alex Szalay, JHU
Optimal TilingOptimal TilingOptimal TilingOptimal Tiling
Fields have 3 degree diameterCenters determined by an optimization procedureA total of 2200 pointings640 fibers assigned simultaneously
Fields have 3 degree diameterCenters determined by an optimization procedureA total of 2200 pointings640 fibers assigned simultaneously
Alex Szalay, JHU
The Mosaic CameraThe Mosaic CameraThe Mosaic CameraThe Mosaic Camera
Alex Szalay, JHU
Photometric CalibrationsPhotometric CalibrationsPhotometric CalibrationsPhotometric Calibrations
The SDSS will create a new photometric system:
u' g' r' i' z'
Primary standards: observed with the USNO 40-inch telescope in Flagstaff
Secondary standards: observed with the SDSS 20-inch telescope at Apache Point – calibrating the SDSS imaging data
The SDSS will create a new photometric system:
u' g' r' i' z'
Primary standards: observed with the USNO 40-inch telescope in Flagstaff
Secondary standards: observed with the SDSS 20-inch telescope at Apache Point – calibrating the SDSS imaging data
Alex Szalay, JHU
The SpectrographsThe SpectrographsThe SpectrographsThe Spectrographs
Two double spectrographs very high throughput two 2048x2048 CCD detectors mounted on the telescope light fed through slithead
Two double spectrographs very high throughput two 2048x2048 CCD detectors mounted on the telescope light fed through slithead
Alex Szalay, JHU
The Fiber Feed SystemThe Fiber Feed SystemThe Fiber Feed SystemThe Fiber Feed System
Galaxy images are captured by optical fibers lined up on the spectrograph slitManually plugged during the day into Al plugboards640 fibers in each bundleThe largest fiber system today
Galaxy images are captured by optical fibers lined up on the spectrograph slitManually plugged during the day into Al plugboards640 fibers in each bundleThe largest fiber system today
Alex Szalay, JHU
Spectrograph StatusSpectrograph StatusSpectrograph StatusSpectrograph Status
Spectrographs: Laboratory observations of solar spectrum First astronomical observations March 1999
Spectrographs: Laboratory observations of solar spectrum First astronomical observations March 1999
Alex Szalay, JHU
JHU ContributionsJHU ContributionsJHU ContributionsJHU Contributions
Fiber spectrographs
P. FeldmanA. UomotoS. FriedmanS. Smee
Fiber spectrographs
P. FeldmanA. UomotoS. FriedmanS. Smee
Science Archive
A. SzalayA. ThakarP. KunsztI. CsabaiGy. SzokolyA. ConnollyA. Chaudhaury
Science Archive
A. SzalayA. ThakarP. KunsztI. CsabaiGy. SzokolyA. ConnollyA. Chaudhaury
Management
T. HeckmanT. PoehlerJ. CrockerA. DavidsenA. UomotoA. SzalayR. Wyse
Management
T. HeckmanT. PoehlerJ. CrockerA. DavidsenA. UomotoA. SzalayR. Wyse
Alex Szalay, JHU
First Light ImagesFirst Light ImagesFirst Light ImagesFirst Light Images
Telescope: First light May 9th 1998 Equatorial scans
Telescope: First light May 9th 1998 Equatorial scans
Alex Szalay, JHU
The First StripesThe First StripesThe First StripesThe First Stripes
Camera: 5 color imaging of >100 square degrees Multiple scans across the same fields Photometric limits as expected
Camera: 5 color imaging of >100 square degrees Multiple scans across the same fields Photometric limits as expected
Alex Szalay, JHU
NGC 2068NGC 2068NGC 2068NGC 2068
Alex Szalay, JHU
UGC 3214UGC 3214UGC 3214UGC 3214
Alex Szalay, JHU
NGC 6070NGC 6070NGC 6070NGC 6070
Alex Szalay, JHU
The First QuasarsThe First QuasarsThe First QuasarsThe First Quasars
Three of the four highest redshift Three of the four highest redshift quasars have been found in the quasars have been found in the
first SDSS test data !first SDSS test data !
Three of the four highest redshift Three of the four highest redshift quasars have been found in the quasars have been found in the
first SDSS test data !first SDSS test data !
Alex Szalay, JHU
SDSS Data FlowSDSS Data FlowSDSS Data FlowSDSS Data Flow
Alex Szalay, JHU
Data Processing PipelinesData Processing PipelinesData Processing PipelinesData Processing Pipelines
Alex Szalay, JHU
Concept of the SDSS ArchiveConcept of the SDSS ArchiveConcept of the SDSS ArchiveConcept of the SDSS Archive
OperationalArchive
(raw + processed data)
Science Archive(products accessible to users)
Other ArchivesOther ArchivesOther Archives
Alex Szalay, JHU
All raw data saved in a tape vault at Fermilab
Object catalog 400 GB parameters of >108 objects
Redshift Catalog 1 GB parameters of 106 objects
Atlas Images 1.5 TB 5 color cutouts of >108 objects
Spectra 60 GB in a one-dimensional form
Derived Catalogs 20 GB - clusters - QSO absorption lines
4x4 Pixel All-Sky Map 60 GB heavily compressed
Object catalog 400 GB parameters of >108 objects
Redshift Catalog 1 GB parameters of 106 objects
Atlas Images 1.5 TB 5 color cutouts of >108 objects
Spectra 60 GB in a one-dimensional form
Derived Catalogs 20 GB - clusters - QSO absorption lines
4x4 Pixel All-Sky Map 60 GB heavily compressed
SDSS Data ProductsSDSS Data ProductsSDSS Data ProductsSDSS Data Products
Alex Szalay, JHU
How will the data be analyzed?How will the data be analyzed?How will the data be analyzed?How will the data be analyzed?
The data are inherently multidimensional=> positions, colors, size, redshift
Improved classifications result in complex N-dimensional volumes=> complex constraints, not ranges
Spatial relations will be investigated=> nearest neighbors=> other objects within a radius
Data Mining: finding the ‘needle in the haystack’=> separate typical from rare=> recognize patterns in the data
Output size can be prohibitively large for intermediate files=> import output directly into analysis tools
The data are inherently multidimensional=> positions, colors, size, redshift
Improved classifications result in complex N-dimensional volumes=> complex constraints, not ranges
Spatial relations will be investigated=> nearest neighbors=> other objects within a radius
Data Mining: finding the ‘needle in the haystack’=> separate typical from rare=> recognize patterns in the data
Output size can be prohibitively large for intermediate files=> import output directly into analysis tools
Alex Szalay, JHU
Geometric ApproachGeometric ApproachGeometric ApproachGeometric Approach
The Main Problem:•fast, indexed, complex searches of Terabytes in k-dim space•searches are not necessary parallel to the axes
=> traditional indexing (b-tree) does not work
The Main Problem:•fast, indexed, complex searches of Terabytes in k-dim space•searches are not necessary parallel to the axes
=> traditional indexing (b-tree) does not work
Geometric Approach:•Use the geometric nature of the k-dimensional data•Quantize data into containers of ‘friends’:
objects of similar colorsclose on the skystored together=> efficient cache performance
•Containers represent a coarse grained density map of the datamultidimensional index tree: k-d tree + r-tree
Geometric Approach:•Use the geometric nature of the k-dimensional data•Quantize data into containers of ‘friends’:
objects of similar colorsclose on the skystored together=> efficient cache performance
•Containers represent a coarse grained density map of the datamultidimensional index tree: k-d tree + r-tree
Alex Szalay, JHU
Geometric IndexingGeometric IndexingGeometric IndexingGeometric Indexing
“Divide and Conquer”“Divide and Conquer” PartitioningPartitioning
3 N M3 N M
HierarchicalTriangular
Mesh
HierarchicalTriangular
Mesh
Split as k-d treeStored as r-tree
of bounding boxes
Split as k-d treeStored as r-tree
of bounding boxes
Using regularindexing
techniques
Using regularindexing
techniques
Attributes Number
Sky Position 3Multiband Fluxes N = 5+Other M= 100+
Attributes Number
Sky Position 3Multiband Fluxes N = 5+Other M= 100+
Alex Szalay, JHU
Sky coordinatesSky coordinatesSky coordinatesSky coordinates
Stored as Cartesian coordinates:projected onto a unit sphere
Longitude and Latitude lines:intersections of planes and the sphere
Boolean combinations:query polyhedron
Stored as Cartesian coordinates:projected onto a unit sphere
Longitude and Latitude lines:intersections of planes and the sphere
Boolean combinations:query polyhedron
Alex Szalay, JHU
Sky PartitioningSky PartitioningSky PartitioningSky Partitioning
Hierarchical Triangular Mesh - based on octahedron
Alex Szalay, JHU
Hierarchical SubdivisionHierarchical SubdivisionHierarchical SubdivisionHierarchical Subdivision
Hierarchical subdivision of spherical trianglesrepresented as a quadtree
In SDSS the tree is 5 levels deep - 8192 triangles
Hierarchical subdivision of spherical trianglesrepresented as a quadtree
In SDSS the tree is 5 levels deep - 8192 triangles
Alex Szalay, JHU
Result of the QueryResult of the QueryResult of the QueryResult of the Query
Alex Szalay, JHU
Magnitudes and Multicolor SearchesMagnitudes and Multicolor SearchesMagnitudes and Multicolor SearchesMagnitudes and Multicolor Searches
Galaxy fluxes• large dynamic range• errors
divergent as x 0 ! 2
22
22
10010 log5.2)/(log5.2
x
xx
x
mm
xffm
2
22
22
10010 log5.2)/(log5.2
x
xx
x
mm
xffm
But: this is an artifact of the logarithm at zero flux,
in flux space the object is well localized
But: this is an artifact of the logarithm at zero flux,
in flux space the object is well localized
For multicolor magnitudes the error contours can be very anisotropic and skewed,
extremely poor localization!
Alex Szalay, JHU
Novel Magnitude ScaleNovel Magnitude ScaleNovel Magnitude ScaleNovel Magnitude Scale
cb
f
1sinh
10ln
5.2
cb
f
1sinh
10ln
5.2
b: softnessc: set to match normal magnitudes
Advantages: monotonic degrades gracefully objects have small error ellipse unified handling of detections
and upper limits!
Disadvantages: unusual
(Lupton, Gunn and Szalay, AJ 99)
Alex Szalay, JHU
Flux IndexingFlux IndexingFlux IndexingFlux Indexing
Split along alternating flux directionsCreate balanced partitionsStore bounding boxes at each stepBuild a 10-12 level tree in each triangle
Split along alternating flux directionsCreate balanced partitionsStore bounding boxes at each stepBuild a 10-12 level tree in each triangle
Alex Szalay, JHU
Therefore: first create a local density and split on its value (Csabai etal 96)
typical (98%) rare (2%)
Therefore: first create a local density and split on its value (Csabai etal 96)
typical (98%) rare (2%)
The SDSS will measure fluxes in 5 bands => asinh magnitudes
Axis-parallel splits in median flux, in 8 separate zones in Galactic latitude
=> 5 dimensional bounding boxes
The SDSS will measure fluxes in 5 bands => asinh magnitudes
Axis-parallel splits in median flux, in 8 separate zones in Galactic latitude
=> 5 dimensional bounding boxes
How to build compact cells?How to build compact cells?How to build compact cells?How to build compact cells?
The fluxes are strongly correlated=> 2 + dimensional distribution of typical objects=> widely scattered rare objects
=> large density contrasts
The fluxes are strongly correlated=> 2 + dimensional distribution of typical objects=> widely scattered rare objects
=> large density contrasts
Alex Szalay, JHU
User InterfaceUser Interface Analysis EngineAnalysis Engine
Master
Objectivity
ObjectivityObjectivity
RAIDRAID
Slave
ObjectivityObjectivity
RAIDRAID
Slave
ObjectivityObjectivity
RAIDRAID
Slave
ObjectivityObjectivity
RAIDRAID
Slave
SX Engine Objectivity Federation
Distributed ImplementationDistributed ImplementationDistributed ImplementationDistributed Implementation
Alex Szalay, JHU
Exploring new methodsExploring new methodsExploring new methodsExploring new methods
New spectral classification techniquesgalaxy spectra can be expressed as a superpositionof a few (<5) principal components
=> objective classification of 1 million spectra!
New spectral classification techniquesgalaxy spectra can be expressed as a superpositionof a few (<5) principal components
=> objective classification of 1 million spectra!
Photometric redshiftsgalaxy colors systematically change with redshift,the SDSS photometry works like a 5-pixel spectrograph
=> z=0.05, but with 100 million objects!
Photometric redshiftsgalaxy colors systematically change with redshift,the SDSS photometry works like a 5-pixel spectrograph
=> z=0.05, but with 100 million objects!
Measuring cosmological parametersbefore: data analysis was limited by small number statisticsafter: dominant errors are systematic (extinction)
=> new analysis methods are required!
Measuring cosmological parametersbefore: data analysis was limited by small number statisticsafter: dominant errors are systematic (extinction)
=> new analysis methods are required!
Alex Szalay, JHU
Photometric redshiftsPhotometric redshiftsPhotometric redshiftsPhotometric redshifts
Multicolor photometry maps physical parametersluminosity Lredshift z spectral type T
Inversion: u’,g’,r’,I’,z’ => z, L, T
Multicolor photometry maps physical parametersluminosity Lredshift z spectral type T
Inversion: u’,g’,r’,I’,z’ => z, L, T
Redshifts are statistical, with large errors: z0.05
The data set is huge, more than 100 million galaxies
Easy to subdivide into coarse z bins, and by type=> study evolution=> enormous volume - 1 Gpc3
Redshifts are statistical, with large errors: z0.05
The data set is huge, more than 100 million galaxies
Easy to subdivide into coarse z bins, and by type=> study evolution=> enormous volume - 1 Gpc3
observed fluxes
Alex Szalay, JHU
Spectra from PhotometrySpectra from PhotometrySpectra from PhotometrySpectra from Photometry
New development:low resolution spectra from multicolor photometrymany galaxies => oversampling => spectra
(Csabai, Budavari, Connolly, Szalay 99)
New development:low resolution spectra from multicolor photometrymany galaxies => oversampling => spectra
(Csabai, Budavari, Connolly, Szalay 99)
Alex Szalay, JHU
Measuring P(k) Measuring P(k) Measuring P(k) Measuring P(k)
Karhunen-Loeve transform:Signal-to-noise eigenmodes of the redshift surveyOptimal extraction of clustering signalMaximal rejection of systematic errors
(Vogeley and Szalay 96, Matsubara, Szalay and Landy 99)
Karhunen-Loeve transform:Signal-to-noise eigenmodes of the redshift surveyOptimal extraction of clustering signalMaximal rejection of systematic errors
(Vogeley and Szalay 96, Matsubara, Szalay and Landy 99)
Pilot project using the Las Campanas Redshift Survey with 22,000 galaxies
Pilot project using the Las Campanas Redshift Survey with 22,000 galaxies
03.003.0
04.004.0
15.014.0
05.005.0
05.005.0
22.019.0
05.005.0
06.006.0
22.020.0
8
14.078.040.0
14.075.031.0
15.082.048.0
Combined
South
North
We simultaneously measure the values of the redshift-distortion parameter (=0.6/b), the normalization (8 ) and the CDM shape parameter ( = h).
We simultaneously measure the values of the redshift-distortion parameter (=0.6/b), the normalization (8 ) and the CDM shape parameter ( = h).
Alex Szalay, JHU
The next generation of astronomical archives with Terabyte catalogs will dramatically change astronomy
top-down designlarge sky coveragebuilt on sound statistical plansuniform, homogeneous, well calibratedwell controlled and documented systematics
The technology to store and index the data is here
Data mining in such vast archives will be a challenge, but possibilities are quite unimaginable
Integrating these archives into a single entity is a project for the whole community
=> Virtual National Observatory
The Age of Mega-SurveysThe Age of Mega-SurveysThe Age of Mega-SurveysThe Age of Mega-Surveys
Alex Szalay, JHU
SummarySummarySummarySummary
The SDSS project combines astronomy, physics, and computer scienceThe SDSS project combines astronomy, physics, and computer science
It promises to fundamentally change our view of the universeIt promises to fundamentally change our view of the universe
It will determine how the largest structures in the universe were formedIt will determine how the largest structures in the universe were formed
Its ‘virtual universe’ can be explored by both scientists and the publicIts ‘virtual universe’ can be explored by both scientists and the public
It will serve as the standard astronomy reference for several decadesIt will serve as the standard astronomy reference for several decades
Through its archive it will create a new paradigm in astronomyThrough its archive it will create a new paradigm in astronomy
Alex Szalay, JHU