1. eScience May 2007 From Photons to Petabytes: Astronomy in the Era of Large Scale Surveys and Virtual Observatories R. Chris Smith NOAO/CTIO, LSST

2. eScience May 2007 Challenges for the Operational VO Providing Content capturing and archiving data from diverse instruments, AND capturing metadata (system & science) to make that data useful Providing Access implementing the VO standards and services, plus network infrastructure, needed for wide access to the content Ensure not only access, but long-term support and documentation of datasets & metadata (curation) Providing User Interfaces and Tools developing and operating user interfaces which enable effective scientific use of ALL of the distributed resources of the VO

3. eScience May 2007 A Case Study: NOAO Data Management Management of data from all NOAO and some affiliated facilities = CONTENT 3 mountaintops (Cerro Tololo, Cerro Pachon, Kitt Peak) 11 telescopes More than 30 instruments Virtual Observatory “back end” = ACCESS Provide effective access to large volume (TBs to PBs) of archived ground-based optical & infrared data and data products through VO standard interfaces and networks Virtual Observatory “front end” = UI and TOOLS Enable science by developing VO user interfaces, tools, and services to work with distributed data sources and large volumes of data

4. eScience May 2007

5. BIG Question: How does this model SCALE? Capturing, moving, & processing the data Making the data AVAILABLE through VO interfaces Making the data USEFUL for scientific analysis Why do we worry about scaling?

6. eScience May 2007 Turning Photons into Petabytes Today MOSAIC, WFI, IMACS: 64 Mpix cameras ~10 to 20 GB/night Builds up quickly! in only 3 years of two MOSAIC cameras ~20TB raw data ~40-60TB processed IMACS image, Las Campanas Observatory (Danny Steeghs, Jan'04)

7. eScience May 2007 Coming Soon: Dark Energy Camera Focal Plane: 64 2K x 4K detectors Plus guiding and WFS 530 Mpix camera

8. eScience May 2007 The Data: Dark Energy Survey Each image = 1GB 350 GB of raw data / night Data must be moved to supercomputer center (NCSA) before next night begins (<24 hours) Need >36Mbps internationally Data must be processed within ~24 hours Need to inform next night’s observing Total raw data after 5 yrs ~0.2 PB TOTAL Dataset 1 to 5 PB Reprocessing planned using TeraGrid resources

9. eScience May 2007 LSST: The Large Synoptic Survey Telescope Survey the entire sky every 3 to 5 nights, to simultaneously detect and study: Dark Matter via Weak gravitational lensing Dark Energy via thousands of SNe per year Potentially hazardous near earth asteroids Tracers of the formation of the solar system Fireworks in the heavens – GRBs, quasars… Periodic and transient phenomena...…the unknown Massively PARALLEL Astronomy

10. eScience May 2007 LSST: The Instrument 8.2m telescope Optimized for WIDE field of view 3.5 degree FOV 3.5 GIGApixel camera Deep images in 15s Able to scan whole sky every 3 to 5 nights

11. eScience May 2007 LSST: Deep, Wide, Fast Field of view (FOV) Keck Telescope 0.2 degrees 10 m 3.5 degrees LSST

12. eScience May 2007 LSST Site: Cerro Pachon, Chile Soar Gemini LSST ~1.5m cal telescope Support LSST site plan El Penon Gemini (South) SOAR

13. eScience May 2007 LSST: Distributed Data Mgmt Long-Haul Communications Data transport & distribution Base Facility Real time processing Mountain Site data acquisition, temp. storage Archive/Data Access Centers Data processing, long term storage, & public access

14. eScience May 2007 LSST: The Data Flow Each image roughly 6.5GB Cadence: ~1 image every 15s 15 to 18 TB per night ALL must be transferred to U.S. “data center” Mtn-base within image timescale (15s), ~10-20Gbps Internationally within 2-10Gbps REAL TIME reduction, analysis, & alerts Send out alerts of transient sources within minutes Provide automatic data quality evaluation, alert to problems Processed data grows to >100TB per night! Just catalogs = Petaybytes per year!

15. eScience May 2007 LSST Needs Archive Center Base Data Access Center

16. eScience May 2007 Turning Photons into Petabytes: Summary Today, ~10 to 20 GB/night MOSAIC, WFI, IMACS: 64 Mpix cameras Soon, ~300 to 500 GB/night VISTA: 67 Mpix camera VST: 256 Mpix camera DECam/DES: 520 Mpix camera On the horizon, ~15 TB/night LSST Project: 3 Gpix camera And these are just survey instruments in Chile!

17. eScience May 2007 DES, LSST, … the REST of the Science? Ongoing (MOSAIC, WFI, IMACS) and future (DES, LSST, etc.) projects will provide PETABYTES of archived data Only a small fraction of the science potential will be realized by the planned investigations How do we maximize the investment in these datasets and provide for their future scientific use?

18. eScience May 2007 VO Challenges Provider Perspective How do we effectively capture, transport, and manage Petabytes of data? Need advanced IT infrastructure How do we provide effective access to Petabytes of data? Need advanced data mining interfaces Fundamentally IT challenges, in support of the astronomical community

19. eScience May 2007 VO Challenges Scientific Perspective Data Discovery From those Petabytes, what data exists that might be useful to help address my scientific query? Data Understanding Which data are best suited for my analysis? Data Movement How do I get the data from where it is to where it is most useful? Data Analysis How do I extract the information I need from the data?

20. eScience May 2007 NVO portal @ NOAO Focus on Scientific USER 4 Keys: Data Discovery, Data Understanding, Data Access, Data Analysis First focus on supporting data DISCOVERY Discovery in spatial coordinates: NOAO Sky Discovery in temporal coordinates: Timeline NOAO NVO portals: http://nvo.noao.edu And for South America… http://nvo.ctio.noao.edu Foundation for exploring partnerships with S.A. communities

21. eScience May 2007 Summary: VO Challenges In Infrastructure Collect and maintain petabytes of content Provide for effective access, including networks, hardware, and software In User Interaction Provide effective user interfaces Support distributed analysis Support large queries across distributed DBs Support statistical analysis and processing across distributed resources (Grid processing & storage) TOOLS & SERVICES to enable SCIENCE

22. eScience May 2007 How? Strategic Partnerships In Local Systems Vendors: Local Storage, Processing, Servers In Remote Systems Distributed computer centers to provide bulk storage, large scale processing Linked together for Grid processing, Grid storage In Connectivity High-speed national and international bandwidth Scientific VO Partners to develop standards, provide tools (IVOA) Developing tools and services optimized for scientific analysis over large datasets (e.g., statistical methods)