Data Intensive Radio Astronomy
en route to the SKA
The rise ofBIG RADIO DATARuss Taylor
Institute for Space Imaging Science
University of Calgary

Technology Challenges en route to SKA• Large collecting Area
• Large field-of-view• Large bandwidth• Power consumption• Massive Data

Bandwidth• Until recently bandwidths of a few 10’s of MHz werestandard– VLA: 50 MHz per IF• New telescopes and upgrades dramatically increase
bandwidth– Arecibo: 300 MHz– ASKAP: 300 MHz– MeerKAT: 770 MHz– JVLA: 8 GHz• At the same time observing in “spectral-line” mode
– for RFI detection and excision (BW extends beyond protected regions)– Avoid signal degradation for wide-field imaging.
– Use of multiple frequencies for visibility sampling (multi-frequencysynthesis)

April	  24,	  2012	  3

WIDAR Correlator Rack Installation at JVLA, Aug 2008

EVLA	  Review	  March	  2009	  4	  Jansky VLA-VLA Comparison
Parameter

VLA

JVLA

FactorCurrent

Point Source Cont. Sensitivity (1σ,12hr.)

10 µJy1 µJy102 µJy

Maximum BW in each polarization

0.1 GHz

8 GHz80

2 GHz# of frequency channels at max. BW

1616,38410244096

Maximum number of freq. channels

512

4,194,304

8192

12,288

Coarsest frequency resolution

50 MHz2 MHz252 MHz

Finest frequency resolution

381 Hz0.12 Hz

3180.12 Hz# of full-polarization spectral windows

2643216(Log) Frequency Coverage (1 – 50 GHz)

22%

100%

5100%

Field of View technologies• Large N small D– 15 m antennas 1 sq deg at 1.4 GHz.

•Arecibo & FAST
– Multiple horn receivers•

Aperture plane arrays– Multiple independent beams over ahemisphere•

Phased Array Feeds– Multiple beams providing large FOVApril	  24,	  2012	  6

FPA Image Outputs

1	  degree	  Sociology of Radio Astronomy• Much of the key science en route to the SKA will beachieved via large-scale survey mode observing programs
executed by globally distributed teams of researchers

Arecibo ALFA Surveys• ALFALFA	  	   	  (HI)	  • PALFA	   	   	  (Pulsars)	  • GALFACTS 	  (Spectro-­‐polarimetry)	  GALFACTS	  and	  PALFA	  Aggregate	  rate	  	  500	  MB/s	  	  LOFAR Survey Science• Sky surveys at 15, 30, 60, 120, 200 MHz– Galaxy formation– Intergalactic magnetic fields– Star formation in early universe– Expansion of discovery parameter space

ASKAP Survey Science••••••••••WALLABY (HI emission)
EMU (continuum)
POSSUM (polarization)
FLASH (HI absorption)
VAST (slow transients and variables)
GASKAP (Galactic HI)
CRAFT (fast transients)
DINGO (Deep HI)
COAST (pulsar and timing survey)
VLBI (high resolution science)
Some project will be commensal

MeerKAT Survey Science••••••••••

Pulsar Timing
LADUMA (Deep HI)
MESMER (High-z CO)
MeerKAT Absorption Line SurveyMHONGOOSE (Nearby HI)
TRAPUM (pulsar search)
MeerKAT HI Survey of Fornax
MeerGAL (Galactic Plane Survey)
MIGHTEE (Deep continuum and polarization)
ThunderKAT (variables and transients)
Some project will be commensal

MeerKAT Large Surveys (43,000 hours allocated)

22	  countries	  13	  SKA Timeline
2012	  2008	  €	  200	  M	  2016	  €	  90	  M	  Site	  Selec,on	  SKA1	  Pre-­‐construcPon	  Preparatory	  Phase	  2020	  €	  350	  M	  €	  1.5	  B	  (1	  &	  2)	  SKA1	  250	  15-­‐m	  Dishes	  +	  	  f=0.1	  Aperture	  Array	  SKA2	  2500	  15-­‐m	  Dishes	  +	  f	  =	  0.5	  Aperture	  Array	  	  0.5	  –	  10	  GHz	  ASKAP	  &	  MeerKAT	  0.1	  –	  0.5	  GHz	  MWA,	  LOFAR,	  PAPER	  1%	  10%	  100%	  Data, data, data, informa…..(gulp).

Survey Raw Data Rates

Terabyte/s	  The Challenge• Survey mode observations drive:– Very high data rates and volumes• Storage, transfer, access• Delivery of data to end users not practical– Complex, multi-purpose, processing and analysis• Processing, analysis, visualization, data mining• Multiple processing and analysis chains– Collaborative execution by globally distributed teams of researchers• Distributed and remote science community• Distributed colllaboration in data processing, analysis and science17

How to handle the Big Data Challenge• Dedicated observatory pipelines to– create science ready output at the observatory and send finishedproducts to users– Being adopted by LOFAR, EVLA, ASKAP,…

• Cons– Monolothic system under control of central “authority” of experts– One-pass through data. No room for iteration and improvement– Disconnects the user community from intellectual development
processing techniques and technologies

How to handle the Big Data ChallengeAn	  alternate	  approach:	  	  cyberinfrastructure	  plaborm	  • Use cloud and web 2.0 technologies to empower the enduser• Turn global resources into the solution– On-line interactive access to a global HPC cloud and data
– Collaborative development of novel pipelines processes– Visualization and visual analytics of very large data
– Creation and ingestion of derived and ancilliary data products– E-science tools for distributed collaboration and analysis

The CyberSKA Project
Initiative to develop a scalable and distributed cyberinfrastructure platform tomeet evolving needs of data-intensive radio astronomy en route to the SKAWestGrid	  UBC	  UBCO	  UofC	  McGill	  Cornell	  20

CyberSKA Development Partners

21

Focus Areas• Collaboration– Portal built on social networking technologies• Data Management– Scalable collaborative access, sharing and searching ofdistributed (BIG) data sets

• Data Processing– Framework for executing algorithms and workflows• Data Visualization and Visual analytics– On-line interactive visualization of remote Big Data

• Third Party Applications– Community driven site with common API

22

CyberSKA High Level Architecture23Collaborative Portal•

Portal built on top of the Elgg open source social networking platform–Provides many features including: tags, bookmarks, profiles, blogs, wikis, contacts, groups, document
sharing, discussions, messaging, calendars, status, activity feeds

24Distributed Data System•

Based on iRODS (Integrated Rule-OrientedData System)– Abstracts data location– Supports data replication / cross-site backup– Efficient WAN data transfer– Rule engine to automate various tasks•

Upload/download tools•Automated mime type recognition•

Automated header extraction and thumbnail
generation– Java Applet / Java Web Start based– Supports “large” data uploads/downloads– For many common file types– FITS and Measurement Set (CASA) image
data or visibility data

On-line visualization of Big Data
VM	  based	  on-­‐line	  interacPve	  visual	  analyPcs	  of	  large,	  mulP-­‐dimensional	  image	  cubes	  • 0.5	  TB	  	  full	  Stokes	  I,	  Q,	  U	  image	  cube	  sets	  • CollaboraPon,	  screen	  sharing,	  etc.	  	  

26Third Party Application Interface••••API for integrating third party / “remote” server applications
Single sign-on to applications enabled using Oauth
Push/pull information and data to/from portal
Current applications include PALFA Candidate Viewer, PALFA Top
Candidates, PALFA Observation Scheduler, PALFA Diagnostics Tool,
GALFACTS Processing Pipeline, Visibility Data Processing Pipeline, SourceCounts, …

27

IVOA Data Query Interface
Astronomical Data
VOTableUI Actions
System Data
CADC

CyberSKA
Validated Metadata

Data Ingest
ServiceVOTable Parsing

CAOM
Database

TAP Services

CyberSKA Search

CADCAdvancedSearch

Data ManagementServiceVisualizationToolDownloData
Processing
ServiceVOSpaceQueryadCyberSKA
User Data

ExternalUserUser

28

CyberSKA IVOA Collection

CyberSKA Usage••

258 members from around the world
40+ “groups” (GALFACTS, PALFA, RM Synthesis, EVLA Deep Polarization
Field, GMRT Deep Polarization Field, CASA Users, …)

30

Imaging Survey Co-development•••

GALFACTS (Arecibo) - 49 collaborators on CyberSKA
Deep Polarization Field Surveys (EVLA, GMRT)
CyberSKA:– Used for sharing documents, creating wiki pages, having discussions andbookmarking resources– Enables on-line visualization of remote data sets
– Access to GALFACTS survey data and third party applications for running dataprocessing pipelines31

Pulsar Survey Co-development• PALFA (Arecibo) – 69 collaborators• CyberSKA:– shared hub for documentation,
meeting minutes, publications andtask lists– Used as an on-line application centre
for single sign-on access to a varietyof third-party applications– Resulted in a significant increase in
the rate of discoveries

32On-going and future work
• Study funded by North American ARC in collaboration withHarvard to adapt on-line visual analytics for ALMA on-line
data system• Completion of user interface for data pipeline tool to allow
user developed pipeline processing• Collaboration with CADC on next generation IVOA
interface to include visibility data sets (CAOM2)• Incorporation of distributed HPC-based cloud architecture
for scalability to multi-site petascale (Compute Canada,IBM Watson).
33

The Square Kilometre Array