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

“Classical” Optical Astronomy

1-4 investigators propose for telescope time Obtain 1 to 5 nights, or 1 to 5 hours!  Oversubscription on largest telescopes (e.g. Gemini) severely limits time per investigator Travel to distant telescope site  or not: Remote Observing, Service Observing, Queue Observe  or not: clouds (OUCH!) Take.5 to 50 GB of data home (on tapes) Reduce & Analyze “by hand”  Extract every detail from those bits  Often takes months per night of data

Optical Windows

Can “classical” techniques answer the BIG questions? Where do we come from?  Star Formation, Nucleosynthesis Are we alone?  Proto-planetary disks, search for planets Where are we going?  Big Bang & the Expansion of the Universe What is the Universe made of?  What types of matter? What types of energy?

Where are we going? A “Repulsive” Result In 1990s, began looking for deceleration Found expansion of Universe is accelerating!!! Implies something NEW! Regions of empty space REPEL each other?  “Cosmological constant”? Einstein’s greatest blunder… OR NOT?!!  Something going on in the vacuum? NEW FUNDAMENTAL PHYSICS!

Today’s BIG Questions: Dark Energy & Dark Matter Dark Energy is the dominant constituent of the Universe. Dark Matter is next. 95% of the Universe is in Dark Energy and Dark Matter, for which we have little or no detailed understanding and 2003 Science breakthroughs of the year

Attacking the Question of Dark Energy “Classical” approach won’t work  Not enough telescope time  Difficult to control calibrations & systematics LARGE SURVEYS  Goal: Provide large, uniform, well calibrated, controlled, and documented datasets to allow for advanced statistical analyses  Control calibrations & systematics to <1%  Larger collaborations provide both manpower and diverse expertise Including traditional astronomers, high-energy physicists, mathematicians, and computer scientists

Sociology of Dark Energy Dark Energy may be pushing the universe APART But it is pulling the Astronomy, High Energy Physics (HEP), and Computer Science (CS) communities TOGETHER  HEP interests in fundamental physics  HEP experience in large datasets  CS interest in CPUs, Storage, Networks, and (of course) algorithms & optimization!

Dark Energy ROADMAP to understanding Today  ESSENCE, large international group of astronomers Coming Soon to a telescope nearby  Dark Energy Survey Camera built by Fermilab, majority DOE funding Data Management System led by NCSA Groups from Spain, United Kingdom, and Brazil recently joined The next BIG step  LSST Camera built by SLAC, Data Mgmt with NCSA, NSF + DOE funding, also inc. LLNL, Brookhaven, others Stepping UP  Space-based work: JDEM (SNAP and/or others) NASA + DOE funding

Today: ESSENCE (+SuperMACHO) Use a LARGE (~200 SNe), UNIFORM set of supernova light curves to allow us to study the evolution of the expansion of the universe  Constrain “w”, the equation of state parameter of Dark Energy, to ~10% 30 half-nights per year for 5 years ( ) Use other half of nights to constrain possible DARK MATTER candidates  The ‘SuperMACHO’ project  Search the Large Magellanic Cloud for microlensing

Searching for Supernovae (and other transients) High-z SN Team

ESSENCE+SuperMACHO The data flows… The telescope  CTIO’s Blanco 4m The camera  MOSAIC 8Kx8K imager (67 megapixels) Exposures of 60s to 400s Collect 20GB of RAW data per night Data must be reduced and analyzed in near REAL TIME (within ~10min) Data ‘Reduction’ = >5x EXPANSION!  Roughly 3TB per year

… and flows MUCH larger data flow than most other astronomical projects With ADDITIONAL complication of real-time reduction & alert requirement  Must plan spectroscopic follow up on LARGEST telescopes (Gemini, Keck, VLT, Magellan, …) We THOUGHT we were ready  A few CPUs (cluster of 20 x 1GHz)  A few disks (4 x 4TB “data bricks”) But…

Challenges Moving the data  From Chile to the U.S. Storing the data  Filling up racks with “data bricks”  Keeping track of the data  Initial database didn’t cut it Reprocessing the data  Pipeline can keep up with real time flow  But need to reprocess past years of data when improvements are made to software

Coming Soon (2009?): Dark Energy Survey Investigate Dark Energy using 4 complementary and independent methods  Various types of distance measurements, based on standard luminosities, standard yardsticks, and standard volumes Combine the results to provide the best (to date) constraints on the equation of state of Dark Energy

The Instrument: Dark Energy Camera Focal Plane: 64 2k x 4k CCDs Plus guiding and WFS 0.5 GIGApixel camera

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

The Large Synoptic Survey Telescope – Massively Parallel Astronomy Survey the entire sky every 4-5 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

LSST: The Instrument 8.2m telescope  Optimized for WIDE field of view (FOV) 3.5 degree FOV 3.5 GIGApixel camera Deep images in 15-30s Able to scan whole sky every 4-5 nights

LSST: Deep, Wide, Fast Field of view (FOV) Keck Telescope 0.2 degrees 10 m 3.5 degrees LSST

LSST Site: Cerro Pachon in Northern Chile Soar Gemini LSST ~1.5m cal telescope Support LSST site plan El Penon Gemini (South) SOAR

LSST: Distributed Data Management 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

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 >3 PB per year!

DES, LSST, … and now for the REST of the Science Ongoing (ESSENCE, SuperMACHO, etc.) 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?

The Virtual Observatory What is VO?  Provides the framework for global access to the various data archives by facilitating the standardization of archiving and data-mining protocols.  Enables data analysis by providing common standards and state-of-the-art analysis tools which work over high-speed wide area networks What is VO not?  An organization funded to provide a single universal archive of all astronomical data  A provider of resources (storage, computation, bandwidth)

VO: A Global Effort BR-VO

VO Challenges Provide Access to the Content  Multiple distributed archives, some on the scale of many petabytes  Archives provide content, the VO knits those resources together Provide the Standards  Allow variety of archives talk to each other  Develop generalized data model(s) for different instruments/different wavelengths

VO Challenges Provide the User Interfaces  Streamline data discovery, data understanding, data movement, and data analysis Support the Analysis  Support large queries across distributed DBs  Support statistical analysis across results (Grid) All the “boring” bits (infrastructure)  Security, handshaking, resource management

Chris Miller/NOAO

VO Case Study: NOAO Data Products Program Management of data from all NOAO and some affiliated facilities  KPNO, including Mayall 4m (MOSAIC, NEWFIRM)  CTIO, including Blanco 4m (MOSAIC, ISPI)  SOAR & WIYN systems Virtual Observatory “back end”; CONTENT  Provide access to large volume (TBs to PBs) of archived ground-based optical & infrared data and data products Virtual Observatory “front end”; UI and TOOLS  Enable science based upon distributed data and data products, developing tools and services

NVO NOAO Focus on Scientific USER First support data DISCOVERY NOAO Supported NVO Portals:   And for S.America…  Mbps for Chilean use!

Many challenges ahead… Security  enforcing proprietary periods while allowing PIs to combine data VOSpace  virtual (and real) distributed workspaces to collect and analyze data from many sites  shared spaces for collaborations Combining GRID storage and CPU resources with VO queries and analysis

Strategic Partnerships In Local Systems  Vendors: Local Storage, Processing, Servers In Remote Systems  Supercomputer center(s) to provide bulk storage, large scale processing (e.g., NCSA, SDSC’s SRB)  Grid processing, storage Connectivity  High-speed national and international bandwidth Scientific  VO Partners to develop standards, provide tools  Providing services to, and collecting feedback from, physics and astronomy user communities  Providing strong VO node in South America