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EStar – Combining Telescopes and Databases Tim Naylor - University of Exeter Iain Steele – Liverpool John Moores University Dave Carter - Liverpool John.

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Presentation on theme: "EStar – Combining Telescopes and Databases Tim Naylor - University of Exeter Iain Steele – Liverpool John Moores University Dave Carter - Liverpool John."— Presentation transcript:

1 eStar – Combining Telescopes and Databases Tim Naylor - University of Exeter Iain Steele – Liverpool John Moores University Dave Carter - Liverpool John Moores University Chris Motram – Liverpool John Moores University Jason Etherton - Liverpool John Moores University Alasdair Allan - University of Exeter

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5 Imagine a system which… Has unified access to observational data, and to telescopes, and to the scientific literature. And has intelligent software to interpret the results (IAs).

6 Scenario 1 – The space density of dwarf novae. Interacting binary stars – important for evolution. Every CCD field taken in the world is compared with SuperCosmos. Objects which brighten above fixed magnitude (say 16 th  M V ) compared with SIMBAD. Known dwarf novae noted; other variables rejected. Historical data searched for new objects, used to identify lightcurve type.

7 Space density of dwarf novae. If cannot be classified, further observations requested. As lightcurve builds up, future observations placed optimally. Object type finally determined. HST parallax requested to confirm distance. Astronomer comes back from long lunch break and writes paper.

8 Scenario 2 – What was that? 02:11:03UT: shutter closes on a WASP image of Centaurus. 02:12:30UT: the data have been processed and a list of positions and magnitudes is available. 02:12:45UT: An astronomer’s intelligent agent discovers a new, bright object is in the data. 02:13:00UT: In response to the IA’s request for confirmation a small telescope slews to acquire another image.

9 Whilst waiting the IA queries SIMBAD and discovers there is no known variable at this point. 02:15:06UT: The new image confirms the object, so the IA requests a spectrum from the Liverpool Telescope. Whilst waiting, the IA pulls all the other available data and papers. 02:22:34UT: The spectrum is odd, there hasn’t been  -ray burst but VISTA shows a very faint red object, mentioned in a paper last year… 02:22:50UT: An astronomer is woken up.

10 How close are we to this? eScience Telescopes for Astronomical Research. Funded as an e-Science demonstrator project by UK DTI. Uses Meade LX200 & ETX telescopes + SBIG or Apogee cameras. Functions across network, with telescopes sending data “we made earlier”. Test on sky later this year.

11 Design Issues. No overall supervisor (scalability). Many telescopes each with own scheduler, which talk to intelligent agents, written mainly in Perl, via RTML and Globus. Intelligent agents also talk to SIMBAD/ADS/USNO A-2/DSS web services. Many intelligent agents and discovery nodes. An IA is intended to do one science job, and probably resides on the astronomer’s computer.

12 Typical Sequence IA opens up with a Globus resource discovery (LDAP), finding each telescope. Asks which nodes can carry out a particular observation (scoring). Requests an observation, which telescope places in queue (scheduling). Data (raw and reduced) made available to IA.

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14 What sort of variable? Mines SIMBAD to find variable stars at this location.

15 How much is known? Mines Astrophysical Data system for papers, and for other data.

16 What Next? See the demo and Scheduling system needs refining. More intelligent IAs. Looking for industrial partners for transfer in both directions (DTI funded). Looking for astronomy partners; telescopes willing to become part of a network. But none of this will work well if VOs and ROs talk different languages.


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