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Telescope Scheduling

Why choose scheduling?. Computer science orientedAlgorithm designSearch and optimizationA real world problemChance of changing the world in a positive wayGeneralityOther telescope scheduling problemsOther scheduling problems. Task defined. I. Improve Lowell Telescope schedulerLowell observat

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Telescope Scheduling

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    1. Telescope Scheduling CASS 2006 Xin Chen Department of Information and Computer Science University of Hawaii

    2. Why choose scheduling? Computer science oriented Algorithm design Search and optimization A real world problem Chance of changing the world in a positive way Generality Other telescope scheduling problems Other scheduling problems

    3. Task defined I. Improve Lowell Telescope scheduler Lowell observatory – astronomical research institute in Flagstaff, Arizona II. Create a scheduler for Pan-STARRS (Panoramic Survey Telescope And Rapid Response System ) An all-sky survey telescope project PS-1 – Replaced LURE on Haleakala, MAUI – First light: June 22, 2006 – Full 1.4GPix camera by fall 2006 – Surveys start early 2007 – Surveys completed early 2010 PS – 4 – Construction to be finished in 2009

    4. Pan-STARRS PS-I

    5. I. Lowell Telescope Scheduler Current status Night-by-night scheduler Database containing requested observations Interface Tasks Improve scheduler Multi-night scheduler Improve interface (ease, adaptive)

    6. II. Pan-STARRS Scheduler Current status Schedule will be based on experience Tasks Scheduler Database User interface TWO telescopes

    7. Challenge – scheduling problem Scheduling Widely exist Extensively studied NP. Often only near-optimal solution. Telescope scheduling Objects, priorities, time duration, weather constraints, background tasks, rescheduling, speed and efficiency.

    8. Will try Heuristic (traditional scheduling solution) Unusual conditions Special events Expert knowledge and experience Brute-force attack Complete enumeration (for small input size) Genetic algorithm (recent experiments) Optimization Needs good chromosome representation and fitness function

    9. Genetic Algorithm (GA) GA flow chart

    10. Genetic Algorithm (GA) GA fitness graph of a simple telescope scheduler.

    11. Programming environment OS: Linux Scheduler: C Database: MySQL Web Interface: Perl/PHP Web server: Apache

    12. Expected Time Table First semester First month: People interview and literature survey Understand the problem Then: finish Pan-STARRS scheduler, database and web interface. Then: Improve both schedulers Documentation goes on with job done Second semester Improve tricky part if possible

    13. Questions Relevant people and literature? Previous work and their effectiveness? What server to use? Input data and judging criteria? How good is good?

    14. Questions and suggestions

    15. References The Lowell Telescope Scheduler: A System to Provide Non-Professional Access to Large Automatic Telescopes. IMSA 2005: 173-177 Technical Report: A Genetic Algorithm for Telescope Scheduling. Bin Li. May, 2004. http://pan-starrs.ifa.hawaii.edu http://en.wikipedia.org/wiki/Pan-STARRS Use of evolutionary algorithms for telescope scheduling. http://www.vanhemert.co.uk/publications/lund2002.Use_of_Evolutionary_Algorithms_for_Telescope_Scheduling.pdf Optimization of telescope scheduling: http://www.edpsciences.org/articles/aa/abs/2003/19/aa2370/aa2370.html Telon: Remote Observatory Control Software. http://phobos.physics.uiowa.edu/tech/software.html The New MAJORDOME: Efficient Scheduling of Autonomous Telescopes. http://www.adass.org/adass/proceedings/adass99/P2-19/ Contingent Planner/Scheduler Technical Approach Details. http://ic.arc.nasa.gov/projects/ai-rovers/contingent-planner/project-plan.html http://www.google.com/search?q=telescope+scheduling&btnG=Google+Search

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