1. Implementing an Observational Grid Eric Saunders Alasdair Allan Tim Naylor University of Exeter Iain Steele Chris Mottram Liverpool John Moores University Tim Jenness Frossie Economou Brad Cavanagh Andy Adamson Joint Astronomy Centre, Hawaii

2. PPARC e-Science Summer School, NeSC, May 2005 1 Aims Create an autonomous, intelligent robotic telescope network Allow new, previously impossible science, and scope for significant optimisations in observing / scheduling –Continuous tracking –Lightcurve analysis –Telescope time is expensive!

3. PPARC e-Science Summer School, NeSC, May 2005 2 Architecture An ‘observational grid’ Agent paradigm Decentralised, flat topology (peer to peer) Telescopes as real-time databases of the sky

4. PPARC e-Science Summer School, NeSC, May 2005 3 The eSTAR network © Nik Szymanek UKIRT at JACH User Agents The Grid Embedded Agent Robonet-1.0 Embedded Agent OGLE Observations Alert Agent GCN Alerts Alert Agent Alert Agent WFCAM The VO

5. PPARC e-Science Summer School, NeSC, May 2005 4 What are ‘agents’? An agent is just software, not magic! Many definitions I shall use: –An entity which encompasses its own flow of control The real ‘intelligence’ comes from relaxing the hardcoding Useful behaviour emerges through agent-agent interactions

6. PPARC e-Science Summer School, NeSC, May 2005 5 Implementation eSTAR written entirely in Perl Node agents at telescope implemented as web services Communication via RTML and WSDL SOAP for the transport protocol Interoperability is important to us!

7. PPARC e-Science Summer School, NeSC, May 2005 6 Encapsulating Expertise Lightcurve analysis: –Stars have spots –Stars rotate –Luminosity varies Image credit: SOHO (ESA & NASA)

8. PPARC e-Science Summer School, NeSC, May 2005 7 Finding stellar periods

9. PPARC e-Science Summer School, NeSC, May 2005 8 Automating period discovery Difficult – many-variable problem. Not well understood, even by astronomers! Ideal problem for robotic, unmanned observation The sampling problem is a schedule optimisation problem – ideal for agents My work: Building the engine at the heart of the eSTAR period discovery agent

10. PPARC e-Science Summer School, NeSC, May 2005 9 Design goals We call the eSTAR environment an ‘observational grid’. Why? –Uniformity – resources present the same interface –Dynamism – resources appear and disappear –Scalabilty – arbitrary resources can be added –Heterogenous – platform / language-independent –Distributed control – Many providers and users –Workflow – service chaining to solve problems

11. PPARC e-Science Summer School, NeSC, May 2005 10 eSTAR and Robonet-1.0 Searching for extra-Solar planets Real time observation follow-up using the same agent software as UKIRT A testbed for our adaptive dataset planning work Photo credit: Dr Robert Smith, Liverpool John Moores University

12. PPARC e-Science Summer School, NeSC, May 2005 11 Future challenges Adding further telescopes would mean the network becomes truly heterogeneous. Can we handle that? Interoperability between existing networks is not yet a solved problem. Standards based, using RTML over SOAP (and VOEvent for notification?) How do we deal with the general case, of smart agents bartering for telescope time?

13. PPARC e-Science Summer School, NeSC, May 2005 12 A Grid Market User Agents The Grid Grid Market Broker Service Proprietary Telescope Network Broker Service Proprietary Telescope Network The Virtual Observatory Broker Service

14. PPARC e-Science Summer School, NeSC, May 2005 13 The HTN Workshop July 18 -21 2005 Aims Establish the standards for interoperability between robotic telescope networks Work towards the establishment of an e-market for the exchange of telescope time Establish the standards for interoperability with the Virtual Observatory (VO) for event notification See htn-workshop2005.ex.ac.ukhtn-workshop2005.ex.ac.uk Science Goal Monitor