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Alex Szalay Department of Physics and Astronomy The Johns Hopkins University

The Sloan Digital Sky Survey. Alex Szalay Department of Physics and Astronomy The Johns Hopkins University. The Sloan Digital Sky Survey. A project run by the Astrophysical Research Consortium (ARC).

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Alex Szalay Department of Physics and Astronomy The Johns Hopkins University

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  1. The Sloan Digital Sky Survey Alex Szalay Department of Physics and Astronomy The Johns Hopkins University

  2. The Sloan Digital Sky Survey A project run by the Astrophysical Research Consortium (ARC) 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 Max Planck Inst, Heidelberg SLOAN Foundation, NSF, DOE, NASA Goal: To create a detailed multicolor map of the Northern Sky over 5 years, with a budget of approximately $80M Data Size: 40 TB raw, 2 TB processed Alex Szalay, JHU

  3. Scientific 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

  4. Cosmology Primer The Universe is expanding: the galaxies move away from us spectral lines are redshifted v = Ho r Hubble’s law The fate of the universe depends on the balance between gravity and the expansion velocity  = density/criticalif  <1, expand forever Most of the mass in the Universe is dark matter, and it may be cold (CDM) d> * The spatial distribution of galaxies is correlated, due to small ripples in the early Universe P(k):power spectrum Alex Szalay, JHU

  5. The ‘Naught’ Problem What are the global parameters of the Universe? H0the Hubble constant 55-75 km/s/Mpc 0the density parameter 0.25-1 0 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! Alex Szalay, JHU

  6. daCosta etal 1995 SDSS Collaboration 2002 deLapparent, Geller and Huchra 1986 Gregory and Thompson 1978 The Cosmic Genome Project The SDSS will create the ultimate mapof the Universe, with much more detailthan any other measurement before Alex Szalay, JHU

  7. Area and Size of Redshift Surveys Alex Szalay, JHU

  8. Clustering 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 x100 1990 x2 1995 ±0.4 1998 ±0.2 1999 ±0.1 2002 ±0.05 Alex Szalay, JHU

  9. Relevant Scales Distances measured in Mpc [megaparsec] 1 Mpc = 3 x 1024 cm 5 Mpc = distance between galaxies 3000 Mpc = scale of the Universe if  >200 Mpc fluctuations have a PRIMORDIAL shape if  <100 Mpcgravity creates sharp features, like walls, filaments and voids Biasing conversion of mass into light is nonlinear light is much more clumpy than the mass Alex Szalay, JHU

  10. The 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

  11. Finding the Most Distant Objects Intermediate and high redshift QSOs Multicolor selection function. Luminosity functions and spatial clustering. High redshift QSO’s (z>5). Alex Szalay, JHU

  12. Features of the SDSS Special 2.5m telescope, located at Apache Point, NM 3 degree field of view. Zero distortion focal plane. Two surveys in one: Photometric survey in 5 bands. Spectroscopic redshift survey. Huge CCD Mosaic 30 CCDs 2K x 2K (imaging) 22 CCDs 2K x 400 (astrometry) Two high resolution spectrographs 2 x 320 fibers, with 3 arcsec diameter. R=2000 resolution with 4096 pixels. Spectral coverage from 3900Å to 9200Å. Automated data reduction Over 100 man-years of development effort. (Fermilab + collaboration scientists) Very high data volume Expect over 40 TB of raw data. About 2 TB processed products Data made available to the public Alex Szalay, JHU

  13. Apache Point Observatory Located in New Mexico, near White Sands National Monument Alex Szalay, JHU

  14. The Telescope Special 2.5m telescope 3 degree field of view Zero distortion focal plane Wind screen moved separately Alex Szalay, JHU

  15. The Photometric Survey 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 Alex Szalay, JHU

  16. Survey Strategy Overlapping 2.5 degree wide stripes Avoiding the Galactic Plane (dust) Multiple exposures on the three Southern stripes Alex Szalay, JHU

  17. The Spectroscopic Survey Measure redshifts of objects  distance SDSS Redshift Survey: 1 million galaxies 100,000 quasars 100,000 stars Two high throughput spectrographs spectral range 3900-9200 Å. 640 spectra simultaneously. R=2000 resolution. Automated reduction of spectra Very high sampling density and completeness Objects in other catalogs also targeted Alex Szalay, JHU

  18. Optimal Tiling Fields have 3 degree diameter Centers determined by an optimization procedure A total of 2200 pointings 640 fibers assigned simultaneously Alex Szalay, JHU

  19. The Mosaic Camera Alex Szalay, JHU

  20. Photometric 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 Alex Szalay, JHU

  21. The Spectrographs Two double spectrographs very high throughput two 2048x2048 CCD detectors mounted on the telescope light fed through slithead Alex Szalay, JHU

  22. The Fiber Feed System Galaxy images are captured by optical fibers lined up on the spectrograph slit Manually plugged during the day into Al plugboards 640 fibers in each bundle The largest fiber system today Alex Szalay, JHU

  23. First Light Images Telescope: First light May 9th 1998 Equatorial scans Alex Szalay, JHU

  24. The First Stripes Camera: 5 color imaging of >100 square degrees Multiple scans across the same fields Photometric limits as expected Alex Szalay, JHU

  25. NGC 2068 Alex Szalay, JHU

  26. UGC 3214 Alex Szalay, JHU

  27. NGC 6070 Alex Szalay, JHU

  28. The First Quasars The four highest redshift quasars have been found in the first SDSS test data ! Alex Szalay, JHU

  29. SDSS T-dwarf (June 1999) Methane/T Dwarf Discovery of several newobjects by SDSS & 2MASS Alex Szalay, JHU

  30. Detection of Gravitational Lensing 28,000 foreground galaxies and 2,045,000 background galaxies in test data(McKay etal 1999) Alex Szalay, JHU

  31. SDSS Data Flow Alex Szalay, JHU

  32. Distributed Collaboration Fermilab U.Chicago U.Washington ESNET I. AdvancedStudy Japan Princeton U. VBNS JHU Apache PointObservatory NMSU USNO Alex Szalay, JHU

  33. Data Processing Pipelines Alex Szalay, JHU

  34. Other Archives Other Archives Other Archives Concept of the SDSS Archive Science Archive (products accessible to users) OperationalArchive (raw + processed data) Alex Szalay, JHU

  35. SDSS Data Products 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 All raw data saved in a tape vault at Fermilab Alex Szalay, JHU

  36. Who will be using the archive? Power Userssophisticated, with lots of resources research is centered around the archive datamoderate number of very intensive queries mostly statistical, large output sizes General Astronomy Public frequent, but casual lookup of objects/regions the archives help their research, but not central to itlarge number of small queries a lot of cross-identification requests Wide Publicbrowsing a ‘Virtual Telescope’ can have large public appeal need special packagingcould be a very large number of requests Alex Szalay, JHU

  37. 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 Alex Szalay, JHU

  38. Geometric 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 • Geometric Approach: • Use the geometric nature of the k-dimensional data • Quantize data into containers of ‘friends’:objects of similar colors close on the sky stored together => efficient cache performance • Containers represent a coarse grained density map of the datamultidimensional index tree: k-d tree + r-tree Alex Szalay, JHU

  39. Attributes Number Sky Position 3 Multiband Fluxes N = 5+ Other M= 100+ Geometric Indexing “Divide and Conquer” Partitioning 3NM HierarchicalTriangular Mesh Split as k-d treeStored as r-treeof bounding boxes Using regularindexing techniques Alex Szalay, JHU

  40. Sky coordinates 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

  41. Sky Partitioning Hierarchical Triangular Mesh - based on octahedron Alex Szalay, JHU

  42. Hierarchical Subdivision Hierarchical subdivision of spherical triangles represented as a quadtree In SDSS the tree is 5 levels deep - 8192 triangles Alex Szalay, JHU

  43. Result of the Query Alex Szalay, JHU

  44. Magnitudes and Multicolor Searches • Galaxy fluxes • large dynamic range • errors • divergent as x 0 ! For multicolor magnitudes the error contours can be very anisotropic and skewed, extremely poor localization! • But: this is an artifact of the logarithm at zero flux, • in flux space the object is well localized Alex Szalay, JHU

  45. Novel Magnitude Scale 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

  46. Flux Indexing Split along alternating flux directions Create balanced partitions Store bounding boxes at each stepBuild a 10-12 level tree in each triangle Alex Szalay, JHU

  47. How to build compact cells? 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 fluxes are strongly correlated=> 2 + dimensional distribution of typical objects => widely scattered rare objects => large density contrasts Therefore: first create a local density and split on its value (Csabai etal 96) typical (98%) rare (2%) Alex Szalay, JHU

  48. Analysis Engine User Interface Query Support Data Warehouse Coarse Grained Design Archive Alex Szalay, JHU

  49. Distributed Implementation User Interface Analysis Engine Master SX Engine Objectivity Federation Objectivity Slave Slave Slave Objectivity Slave Objectivity Objectivity RAID Objectivity RAID RAID RAID Alex Szalay, JHU

  50. JHU Contributions • Fiber spectrographs • P. FeldmanA. UomotoS. FriedmanS. Smee • Science Archive • A. SzalayA. ThakarP. Kunszt • I. CsabaiGy. SzokolyA. ConnollyA. Chaudhaury • A lot of help from • Jim Gray, Microsoft • Management • T. HeckmanT. PoehlerA. DavidsenA. UomotoA. Szalay Alex Szalay, JHU

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