1. 1 Databases Meet Astronomy a db view of astronomy data Jim Gray and Don Slutz Microsoft Research Collaborating with: Alex Szalay, Peter Kunszt, Ani Thakar @ JHU Robert Brunner, Roy Williams @ Caltech George Djorgovski, Julian Bunn @ Caltech

2. 2 Outline Astronomy data The Virtual Observatory Concept The Sloan Digital Sky Survey

3. 3 Astronomy Data In the “old days” astronomers took photos. Starting in the 1960’s they began to digitize. New instruments are digital (100s of GB/nite) Detectors are following Moore’s law. Data avalanche: double every 2 years Total area of 3m+ telescopes in the world in m 2, total number of CCD pixels in megapixel, as a function of time. Growth over 25 years is a factor of 30 in glass, 3000 in pixels. Courtesy of Alex Szalay

4. 4 Astronomy Data Astronomers have a few Petabytes now. –1 pixel (byte) / sq arc second ~ 4TB –Multi-spectral, temporal, … → 1PB They mine it looking for new (kinds of) objects or more of interesting ones(quasars), density variations in 400-D space correlations in 400D space Data doubles every 2 years. Data is public after 2 years. So, 50% of the data is public. Some have private access to 5% more data. So: 50% vs 55% access for everyone

5. 5 Astronomy Data But….. How do I get at that 50% of the data? Astronomers have culture of publishing. –FITS files and many tools. http://fits.gsfc.nasa.gov/fits_home.html http://fits.gsfc.nasa.gov/fits_home.html –Encouraged by NASA. But, data “details” are hard to document. Astronomers want to do it but it is VERY hard. (What programs where used? what were the processing steps? How were errors treated?…) The optimistic hope: XML is the answer. The reality: xml is syntax and tools: FITS on XML will be good but….. Explaining the data will still be very difficult.

6. 6 Astronomy Data And by the way, few astronomers have a spare petabyte of storage in their pocket. But that is getting better: -Public SDSS is 5% of total -Public SDSS is ~50GB -Fits on a 200$ disk drive today. -(more on that later). -THESIS: Challenging problems are publishing data providing good query & visualization tools

7. 7 Astronomy Data Lots of it (petabytes) Hundreds of dimensions per object Cross-correlation is challenging because –Multi-resolution –Time varying –Data is dirty (cosmic rays, airplanes…)

8. 8 The Multiwavelength Crab Nebulae X-ray, optical, infrared, and radio views of the nearby Crab Nebula, which is now in a state of chaotic expansion after a supernova explosion first sighted in 1054 A.D. by Chinese Astronomers. Slide courtesy of Robert Brunner @ CalTech. Crab star 1053 AD

9. 9 M51: in many wavelengths NVSS 20cm WENSS 92cm GB 6cm IRAS 100  ROSAT ~keVDSS Optical 2MASS 2  IRAS 25  Slide courtesy of Robert Brunner @ CalTech.

10. 10 Even in “optical” images are very different Optical Near-Infrared Galaxy Image Mosaics B J R F I N J H K Slide courtesy of Robert Brunner @ CalTech.

11. 11 Exploring Parameter Space Given an arbitrary parameter space: Data Clusters Points between Data Clusters Isolated Data Clusters Isolated Data Groups Holes in Data Clusters Isolated Points Nichol et al. 2001 Slide courtesy of Robert Brunner @ CalTech.

12. 12 Survey Cross-Identification Billions of Sources High Source Densities Multi-Wavelength: Radio to  -Ray All Sky - Thousands of Sq. Degrees Computational Challenge Probabilistic Associations Optimized Likelihood Ratios A Priori Astrophysical Knowledge Important Secondary Parameters Temporal Variability Dynamic & Static Associations User-Defined Cross-Identification Algorithms Radio Survey Cross-Identification: Steep Spectrum Sources Optical-Infrared-Radio: Quasar-Environment Survey Optical-Infrared-X-Ray: Serendipitous Chandra Identification Slide courtesy of Robert Brunner @ CalTech.

13. 13 Data Federation: A Computational Challenge 2MASS vs. DPOSS Cross-identification: 2MASS J < 15 DPOSS I N < 18

14. 14 Outline Astronomy data The Virtual Observatory Concept The Sloan Digital Sky Survey

15. 15 Virtual Observatory http://www.astro.caltech.edu/nvoconf/ http://www.voforum.org/ http://www.astro.caltech.edu/nvoconf/ http://www.voforum.org/ Premise: Most data is (or could be online) So, the Internet is the world’s best telescope: –It has data on every part of the sky –In every measured spectral band: optical, x-ray, radio.. –As deep as the best instruments (2 years ago). –It is up when you are up. The “seeing” is always great (no working at night, no clouds no moons no..). –It’s a smart telescope: links objects and data to literature on them.

16. 16 Virtual Observatory The Age of Mega-Surveys Large number of new surveys –multi-TB in size, 100 million objects or more –individual archives planned, or under way –Data publication an integral part of the survey –Software bill a major cost in the survey Multi-wavelength view of the sky –more than 13 wavelength coverage in 5 years Impressive early discoveries –finding exotic objects by unusual colors L,T dwarfs, high-z quasars –finding objects by time variability gravitational micro-lensing MACHO 2MASS DENIS SDSS PRIME DPOSS GSC-II COBE MAP NVSS FIRST GALEX ROSAT OGLE... MACHO 2MASS DENIS SDSS PRIME DPOSS GSC-II COBE MAP NVSS FIRST GALEX ROSAT OGLE... Slide courtesy of Alex Szalay, modified by jim

17. 17 Virtual Observatory Federating the Archives The next generation mega-surveys are different –top-down design –large sky coverage –sound statistical plans –well controlled/documented data processing Each survey has a publication plan Data mining will lead to stunning new discoveries Federating these archives  Virtual Observatory Slide courtesy of Alex Szalay

18. 18 Virtual Observatory and Education In the beginning science was empirical. Then theoretical branches evolved. Now, we have a computational branches. –The computational branch has been simulation –It is becoming data analysis/visualization The Virtual Observatory can be used to –Teach astronomy: make it interactive, demonstrate ideas and phenomena –Teach computational science skills

19. 19 Virtual Observatory Challenges Size : multi-Petabyte 40,000 square degrees is 2 Trillion pixels –One band (at 1 sq arcsec) 4 Terabytes –Multi-wavelength 10-100 Terabytes –Time dimension >> 10 Petabytes –Need auto parallelism tools Unsolved MetaData problem –Hard to publish data & programs –Hard to find/understand data & programs Current tools inadequate –new analysis & visualization tools Transition to the new astronomy –Sociological issues

20. 20 Demo of Virtual Sky Roy Williams @ Caltech Palomar Data with links to NED. Shows multiple themes, shows link to other sites (NED, VizeR, Sinbad, …) http://virtualsky.org/servlet/Page?T=3&S=21&P=1&X=0&Y=0&W=4&F=1 And NED @ http://nedwww.ipac.caltech.edu/index.htmlhttp://nedwww.ipac.caltech.edu/index.html

21. 21 Demo of Sky Server Alex Szalay of Johns Hopkins built SkyServer (based on TerraServer design). http://skyserver.fnal.gov/

22. 22 What Next? (after the queries, after the web server) How to federate the Archives to make a VO? Send XML: a non-answer equivalent to “send unicode” Define a set of Astronomy Objects and methods. –Based on UDDI, WSDL, SOAP. –Each archive is a service We have started this with TerraService –http://TerraService.net/ shows the idea.http://TerraService.net/ –Working with Caltech (Brunner, Williams, Djorgovski, Bunn) and JHU (Szalay et al) on this

23. 23 3-steps to Virtual Observatory Get SDSS and Palomar online –Alex Szalay, Jan Vandenberg, Ani Thacker…. –Roy Williams, Robert Brunner, Julian Bunn Do queries and crossID matches with CalTech and SDSS to expose –Schema, Units,… –Dataset problems –the typical use scenarios. Implement WebService that lets you get data from both CalTech and SDSS (WSDL/SOAP/…)

24. 24 SkyServer as a WebServer WSDL+SOAP just add details Archive ss = new VOService(SkyServer); Attributes A[] = ss.GetObjects(ra,dec,radius) … ?? What are the objects (attributes…)? ?? What are the methods (GetObjects()...)? ?? Is the query language SQL or Xquery or what?

25. 25 Outline Astronomy data The Virtual Observatory Concept The Sloan Digital Sky Survey

26. 26 Sloan Digital Sky Survey http://www.sdss.org/ http://www.sdss.org/ For the last 12 years a group of astronomers has been building a telescope (with funding from Sloan Foundation, NSF, and a dozen universities). 90M$. Last year was engineer, calibrate, commission They are making the calibration data public. –5% of the survey, 600 sq degrees, 15 M objects 60GB. –This data includes most of the known high z quasars. –It has a lot of science left in it but… that is just the start. Now the data is arriving: –250GB/nite (20 nights per year). –100 M stars, 100 M galaxies, 1 M spectra. http://www.sdss.org/ and http://www.sdss.jhu.edu/http://www.sdss.org/http://www.sdss.jhu.edu/

27. 27 SDSS what I have been doing Work with Alex Szalay, Don Slutz, and others to define 20 canonical queries and 10 visualization tasks. Don Slutz did a first cut of the queries, I’m continuing that work. Working with Alex Szalay on building Sky Server and making data it public (send out 80GB SQL DBs)

28. 28 Two kinds of SDSS data 15M Photo Objects ~ 400 attributes 20K Spectra with ~10 lines/ spectrum

29. 29 Spatial Data Access (Szalay, Kunszt, Brunner) http://www.sdss.jhu.edu/ look at the HTM link http://www.sdss.jhu.edu/ Implemented Hierarchical Triangular Mesh (HTM) as table-valued function for spatial joins. Every object has a 20-deep Mesh ID. Given a spatial definition: Routine returns up to 500 covering triangles. Spatial query is then up to 500 range queries. Very fast: 1,000s of triangles per second.

30. 30 The 20 Queries Q11: Find all elliptical galaxies with spectra that have an anomalous emission line. Q12: Create a grided count of galaxies with u-g>1 and r<21.5 over 60<declination<70, and 200<right ascension<210, on a grid of 2’, and create a map of masks over the same grid. Q13: Create a count of galaxies for each of the HTM triangles which satisfy a certain color cut, like 0.7u-0.5g-0.2i<1.25 && r<21.75, output it in a form adequate for visualization. Q14: Find stars with multiple measurements and have magnitude variations >0.1. Scan for stars that have a secondary object (observed at a different time) and compare their magnitudes. Q15: Provide a list of moving objects consistent with an asteroid. Q16: Find all objects similar to the colors of a quasar at 5.5<redshift<6.5. Q17: Find binary stars where at least one of them has the colors of a white dwarf. Q18: Find all objects within 30 arcseconds of one another that have very similar colors: that is where the color ratios u-g, g-r, r-I are less than 0.05m. Q19: Find quasars with a broad absorption line in their spectra and at least one galaxy within 10 arcseconds. Return both the quasars and the galaxies. Q20: For each galaxy in the BCG data set (brightest color galaxy), in 160<right ascension<170, -25<declination<35 count of galaxies within 30"of it that have a photoz within 0.05 of that galaxy. Q1: Find all galaxies without unsaturated pixels within 1' of a given point of ra=75.327, dec=21.023 Q2: Find all galaxies with blue surface brightness between and 23 and 25 mag per square arcseconds, and -10<super galactic latitude (sgb) <10, and declination less than zero. Q3: Find all galaxies brighter than magnitude 22, where the local extinction is >0.75. Q4: Find galaxies with an isophotal surface brightness (SB) larger than 24 in the red band, with an ellipticity>0.5, and with the major axis of the ellipse having a declination of between 30” and 60”arc seconds. Q5: Find all galaxies with a deVaucouleours profile (r ¼ falloff of intensity on disk) and the photometric colors consistent with an elliptical galaxy. The deVaucouleours profile Q6: Find galaxies that are blended with a star, output the deblended galaxy magnitudes. Q7: Provide a list of star-like objects that are 1% rare. Q8: Find all objects with unclassified spectra. Q9: Find quasars with a line width >2000 km/s and 2.5<redshift<2.7. Q10: Find galaxies with spectra that have an equivalent width in Ha >40Å (Ha is the main hydrogen spectral line.) Also some good queries at: http://www.sdss.jhu.edu/ScienceArchive/sxqt/sxQT/Example_Queries.html http://www.sdss.jhu.edu/ScienceArchive/sxqt/sxQT/Example_Queries.html

31. 31 An easy one Q7: Provide a list of star-like objects that are 1% rare. Found 14,681 buckets, first 140 buckets have 99% time 104 seconds Disk bound, reads 3 disks at 68 MBps. Selectcast((u-g) as int) as ug, cast((g-r) as int) as gr, cast((r-i) as int) as ri, cast((i-z) as int) as iz, count(*) as Population from stars group bycast((u-g) as int), cast((g-r) as int), cast((r-i) as int), cast((i-z) as int) order by count(*)

32. 32 Another easy one Q15: Provide a list of moving objects consistent with an asteroid. Looks hard but there are 5 pictures of the object at 5 different times (colors) and so can compute velocity. Image pipeline computes velocity. Computing it from the 5 color x,y would also be fast Finds 2167 objects in 7 minutes, 70MBps. selectobject_id, -- return object ID sqrt(power(rowv,2)+power(colv,2)) as velocity from sxPhotObj -- check each object. where (power(rowv,2) + power(colv, 2)) > 50 -- square of velocity and rowv >= 0 and colv >=0 -- negative values indicate error

33. 33 A Hard One Q14: Find stars with multiple measurements that have magnitude variations >0.1. This should work, but SQL Server does not allow table values to be piped to table-valued functions. select S.object_ID, S1.object_ID-- return stars that from StarsS,-- S is a star getNearbyObjEq(s.ra, s.dec, 0.017) as N -- N within 1 arcsec (3 pixels) of S. Stars S1-- N == S1 (S1 gets the colors) where S.Object_ID < N.Object_ID-- S1 different from S == N and N.Type = dbo.PhotoType('Star')-- S1 is a star (an optimization) and N.object_ID = S1.Object_ID-- N == S1 and ( abs(S.u-S1.u) > 0.1 -- one of the colors is different. or abs(S.g-S1.g) > 0.1 or abs(S.r-S1.r) > 0.1 or abs(S.i-S1.i) > 0.1 or abs(S.z-S1.z) > 0.1 ) order by S.object_ID, S1.object_ID-- group the answer by parent star. Returns a table of nearby objects

34. 34 A Hard one: Second Try Q14: Find stars with multiple measurements that have magnitude variations >0.1. ------------------------------------------------------------------------------- -- Table-valued function that returns the binary stars within a certain radius -- of another (in arc-minutes) (typically 5 arc seconds). -- Returns the ID pairs and the distance between them (in arcseconds). create function BinaryStars(@MaxDistanceArcMins float) returns @BinaryCandidatesTable table( S1_object_ID bigint not null, -- Star #1 S2_object_ID bigint not null, -- Star #2 distance_arcSec float) -- distance between them as begin declare @star_ID bigint, @binary_ID bigint;-- Star's ID and binary ID declare @ra float, @dec float;-- Star's position declare @u float, @g float, @r float, @i float,@z float; -- Star's colors ----------------Open a cursor over stars and get position and colors declare star_cursor cursor for select object_ID, ra, [dec], u, g, r, i, z from Stars; open star_cursor; while (1=1)-- for each star begin -- get its attribues fetch next from star_cursor into @star_ID, @ra, @dec, @u, @g, @r, @i, @z; if (@@fetch_status = -1) break;-- end if no more stars insert into @BinaryCandidatesTable -- insert its binaries select @star_ID, S1.object_ID, -- return stars pairs sqrt(N.DotProd)/PI()*10800 -- and distance in arc-seconds from getNearbyObjEq(@ra, @dec, -- Find objects nearby S. @MaxDistanceArcMins) as N, -- call them N. Stars as S1-- S1 gets N's color values where @star_ID < N.Object_ID-- S1 different from S and N.objType = dbo.PhotoType('Star')-- S1 is a star and N.object_ID = S1.object_ID-- join stars to get colors of S1==N and (abs(@u-S1.u) > 0.1 -- one of the colors is different. or abs(@g-S1.g) > 0.1 or abs(@r-S1.r) > 0.1 or abs(@i-S1.i) > 0.1 or abs(@z-S1.z) > 0.1 ) end;-- end of loop over all stars -------------- Looped over all stars, close cursor and exit. close star_cursor;-- deallocate star_cursor; return;-- return table end-- end of BinaryStars GO select * from dbo.BinaryStars(.05) Write a program with a cursor, ran for 2 days

35. 35 A Hard one: Third Try Q14: Find stars with multiple measurements that have magnitude variations >0.1. Use pre-computed neighbors table. Ran in 17 minutes, found 31k pairs. ================================================================================== -- Plan 2: Use the precomputed neighbors table select top 100 S.object_ID, S1.object_ID,-- return star pairs and distance str(N.Distance_mins * 60,6,1) as DistArcSec from Stars S,-- S is a star Neighbors N,-- N within 3 arcsec (10 pixels) of S. Stars S1-- S1 == N has the color attibutes where S.Object_ID = N.Object_ID-- connect S and N. and S.Object_ID < N.Neighbor_Object_ID-- S1 different from S and N.Neighbor_objType = dbo.PhotoType('Star')-- S1 is a star (an optimization) and N.Distance_mins <.05-- the 3 arcsecond test and N.Neighbor_object_ID = S1.Object_ID-- N == S1 and ( abs(S.u-S1.u) > 0.1 -- one of the colors is different. or abs(S.g-S1.g) > 0.1 or abs(S.r-S1.r) > 0.1 or abs(S.i-S1.i) > 0.1 or abs(S.z-S1.z) > 0.1 ) -- Found 31,355 pairs (out of 4.4 m stars) in 17 min 14 sec.

36. 36 The Pain of Going Outside SQL (its fortunate that all the queries are single statements) Count parent objects 503 seconds for 14.7 M objects in 33.3 GB 66 MBps IO bound (30% of one cpu) 100 k records/cpu sec Use a cursor No cpu parallelism CPU bound 6 MBps, 2.7 k rps 5,450 seconds (10x slower) select count(*) from sxPhotoObj where nChild > 0 declare @count int; declare @sum int; set @sum = 0; declare PhotoCursor cursor for select nChild from sxPhotoObj; open PhotoCursor; while (1=1) begin fetch next from PhotoCursor into @count; if (@@fetch_status = -1) break; set @sum = @sum + @count; end close PhotoCursor; deallocate PhotoCursor; print 'Sum is: '+cast(@sum as varchar(12))

37. 37 Summary of Current Status 19 of 20 queries run (still need to check the science) Also 14 of 15 “user” queries Run times: on 3k$ PC (2 cpu, 4 disk, 256MB) ~100 IO/cpu sec ~5MB/cpu sec

38. 38 Summary of Current Status 16 of the queries are simple 2 are iterative, 2 are unknown Many are sequential one-pass and two-pass over data Covering indices make scans run fast Table valued functions are wonderful but limitations on parameters are a pain. Counting is VERY common. Binning ( grouping by some set of attributes) is common Did not request cube, but that may be cultural.

39. 39 Reflections on the 20 Queries Data loading/scrubbing is labor intensive & tedious –AUTOMATE!!! This is 5% of the data, and some queries take an hour. But this is not tuned (disk bound). All queries benefit from parallelism (both disk and cpu) (if you can state the query right, e.g. inside SQL). Parallel database machines will do great on this: –Hash machines –Data pumps –See paper in word or pdf on my web site.word pdf Bottom line: SQL looks good. Once you get the answers, you need visualization

40. 40 Call to Action If you do data visualization: we need you (and we know it). If you do databases: here is some data you can practice on. If you do distributed systems: here is a federation you can practice on. If you do astronomy educational outreach here is a tool for you. The astronomy folks are very good, and very smart, and a pleasure to work with, and the questions are cosmic, so …