1. 20 Nov, 2002Virtual Molonglo Observatory1 “The VO in Australia” Melbourne Nov. 28/29 2002  What is the AVO?  How did it develop - Grid computing – particle physics  Current status of International VO projects (http://www.ivoa.net)  Role for Australian Astronomy?  Opportunities & challenges

2. 20 Nov, 2002Virtual Molonglo Observatory2 What is the Virtual Observatory?  NOT one project or the Web  Distributed CPU – AVO, NVO, ASTROGRID  Distributed data – images, catalogues, spectra, simulations & models  Distributed software – assorted acronyms  Resource broker, road map, nodes

3. 20 Nov, 2002Virtual Molonglo Observatory3 What’s it all about? Grid computing deals with coordinated resource sharing and problem solving in dynamic, multi- institutional virtual organisations. The resources are compute power, software, data and collaboration tools.

4. 20 Nov, 2002Virtual Molonglo Observatory4 Some statistics on doubling times  Computing power (Moore’s law): 18 mths  Bandwidth (Nielsen’s law): 20 mths  Data archive size: 12 mths  Number of websites: 9 mths

5. 20 Nov, 2002Virtual Molonglo Observatory5 Challenges & responses  Slow CPU growth distributed computing  Limited BW information hierarchies  Limited storage distributed data  Data diversity interoperability  SOLUTION: GRID COMPUTING

6. 20 Nov, 2002Virtual Molonglo Observatory6 Technical Update  Big commitment in Europe & USA  Wide applications – business & science  VO-compliance & VO-table  Issues of access, security, universal querator, resource broker

7. 20 Nov, 2002Virtual Molonglo Observatory7 Role of Australian Astronomy  Workshop focus on data and tools  Examples of current possibilities  Challenges and opportunities

8. 20 Nov, 2002Virtual Molonglo Observatory8 AVO Project Management Functional Requirements: A First Draft Immediate processing of data from sensors (all s) Formats for raw data in sensor databases Transparent access to all databases Correlation of data sets across databases Facilitation and acceleration of the scientific method using all databases Gavin Thoms 27 November 2002

9. 20 Nov, 2002Virtual Molonglo Observatory9 1. AAO & the IVOA - Strategy  Build/continue alliances with key groups  Assist in development of VO standards  Build VO-compliance into data & products  Facilitate development of analysis tools

10. 20 Nov, 2002Virtual Molonglo Observatory10

11. 20 Nov, 2002Virtual Molonglo Observatory11 The Way forward: ARC grant for 2003 (1.5FTE@AAO)  Incorporate 2dF survey into VO-table (milestone: demo at IAU GA)  Integrate 2dF spectra & catalogue server (milestone: end 2003)  VO-compliance for 6dF from start (milestone: April 2003)  Route map for AAO VO-compliance (milestone: end 2003)

12. 20 Nov, 2002Virtual Molonglo Observatory12 2. Contribution from the Molonglo Observatory  Image availability - data calibration & quality  Source catalogues – integrity and interpretation  What is raw data? Case study at 408 MHz

13. 20 Nov, 2002Virtual Molonglo Observatory13 Response Classification with a Decision Tree Blue ellipses - Sources Red ellipses - Artefacts

14. 20 Nov, 2002Virtual Molonglo Observatory14 Current data pipeline  Automated observations  Manual transport of data (CDs) to Sydney  Customised analysis software programs  Image archive & source catalogue  Processed data back to Molonglo & Web  Resource intensive

15. 20 Nov, 2002Virtual Molonglo Observatory15 3. Machine Learning techniques  Goal – multiwavelength correlations  Problem – database mismatches  Traditional methods – closest position & other information

16. 20 Nov, 2002Virtual Molonglo Observatory16 X Y (A) (B) RADIO: HIPASS 21cm survey OPTICAL: SuperCOSMOS 10 arc min error diameter The correlation problem: which is the radio source?

17. 20 Nov, 2002Virtual Molonglo Observatory17 Use Machine Learning  Data vectors from catalogues  Radio: RA, Dec, velocity, velocity width, flux  Optical: (RA, Dec, B,R,I mags, shape)  N  Training sets  Optical counterparts with measured velocities  Machine learning  Support Vector Machine  Use all parameters for the classification: new physics?  Quadratic programming problem, so unique solutions

18. 20 Nov, 2002Virtual Molonglo Observatory18 4. Future: direct image analysis  Handwritten postcode recognition  US Postal Service database: each digit 16×16 pixels  7,300 training patterns, 2,000 test patterns  Classifier % Error  Decision tree 16.2  5-layer neural net 5.1  Support vector machine 4.1  Human 2.5  Direct analysis of optical pixel data?  Established for morphological galaxy classification  Too many pixels for radio identification problems?

19. 20 Nov, 2002Virtual Molonglo Observatory19 5. Example element of e-Astronomy Australia Build a pipeline processor (running aips++) to process radio synthesis data from ATCA archive on the fly  User can choose parameters of image  Field centre  Field size  Optimise algorithm for science question being asked  Can use latest version of calibration algorithm  Expert users can tweak parameters

20. 20 Nov, 2002Virtual Molonglo Observatory20 Goals of e-Astronomy Australia  Survey and archive data from Australian telescopes available to all IVO users  Prospects to put full ATCA archive online  Set up datagrid and compute grid to give Australian astronomers access to IVO resources  Help develop techniques, protocols, etc for the IVO

21. 20 Nov, 2002Virtual Molonglo Observatory21 6. Tools – new and used  FITS – successful data format – keep?  Astronomy co-ordinate systems – several in use – IAU working group  VOtable – flexibility, greater complexity, incorporate current protocols

22. 20 Nov, 2002Virtual Molonglo Observatory22 7. New multicolour Survey  Imaging survey with Great Melbourne Telescope  A TRAGEDY!

23. 20 Nov, 2002Virtual Molonglo Observatory23 Discussion: paradigm for a small country 1.Identify strengths or special roles in the international context 2.Identify any major international partners gains from the involvement 3.Identify gains for the small country from involvement in the project 4.Identify a realistic niche for a significant contribution 5.If any of 1- 4 are missing, withdraw!

24. 20 Nov, 2002Virtual Molonglo Observatory24 Challenges & Opportunities  Continue training of future astronomers  Need resources to maintain and upgrade databases & fund future instruments  Cross-discipline collaborations  Maintain role in observational science  FIND A NICHE!

25. 20 Nov, 2002Virtual Molonglo Observatory25 Where to now?  LIEF grant for 1 year – new grants?  Raise visibility in Europe, USA programs  Cross discipline links – herbarium, medical centre, particle physics  Identify areas of contribution to international VO – spectroscopy?  http://www.aus-vo.org (David Barnes) http://www.aus-vo.org

26. 20 Nov, 2002Virtual Molonglo Observatory26 Conclusions GOAL: To develop tools, data and organisational structures to facilitate international collaborations and individual research on multidimensional archives operating as a VO.