Presentation on theme: "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."— Presentation transcript:
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
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
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.
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
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
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
20 Nov, 2002Virtual Molonglo Observatory7 Role of Australian Astronomy Workshop focus on data and tools Examples of current possibilities Challenges and opportunities
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
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
20 Nov, 2002Virtual Molonglo Observatory10
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)
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
20 Nov, 2002Virtual Molonglo Observatory13 Response Classification with a Decision Tree Blue ellipses - Sources Red ellipses - Artefacts
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
20 Nov, 2002Virtual Molonglo Observatory15 3. Machine Learning techniques Goal – multiwavelength correlations Problem – database mismatches Traditional methods – closest position & other information
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?
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
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?
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 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
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
20 Nov, 2002Virtual Molonglo Observatory22 7. New multicolour Survey Imaging survey with Great Melbourne Telescope A TRAGEDY!
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!
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!
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
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.