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GBT Dynamic Scheduling System (DSS) Dana Balser, Jim Braatz, Mark Clark, Jim Condon, Ray Creager, Mike McCarty, Ron Maddalena, Paul Marganian, Karen O’Neil,

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Presentation on theme: "GBT Dynamic Scheduling System (DSS) Dana Balser, Jim Braatz, Mark Clark, Jim Condon, Ray Creager, Mike McCarty, Ron Maddalena, Paul Marganian, Karen O’Neil,"— Presentation transcript:

1 GBT Dynamic Scheduling System (DSS) Dana Balser, Jim Braatz, Mark Clark, Jim Condon, Ray Creager, Mike McCarty, Ron Maddalena, Paul Marganian, Karen O’Neil, Eric Sessoms, Amy Shelton

2 Nomenclature Butler GBT Open Sessions Windowed Sessions Fixed Sessions

3 Observing Scheduling Algorithm 24-48 Hours in Advance Scheduling Probabilities Post-Observation Reports Data Collection Advance of Semester Reports of schedule, logs, time lost, etc Phase II data collection Sensitivity calculator Time availability prediction Proposal Submission Tool Science grades How does it work? Historical probabilities Weather forecasts Monitor schedule Backup Project run Observing scripts Autoscheduler Run Scheduler modifies and approves Notification sent Provided outside the DSS Provided by the DSS Requirements Scheduling observers, not scripts Observers retain control Minimum of 24 hours advance notice for observers Wide array of hardware Cannot increase workload of staff or observers

4 Atmospheric Effects Condon & Balser (2011)

5 Maddalena Weather Forecasts

6 Atmospheric Stability Maddalena; Balser (2011) Pyrgeometer: non-imaging device sensitive to 4.5-40 micron over 150 deg fov.

7 Wind Effects Condon (2003)

8 Weather Forecasts: wind Obs Wind Speed (m/s) Forecast Wind Speed (m/s) Day Night Balser (2010); Maddalena

9 Solar Heating rms 330 micron rms 220 micron Nikolic et al. (2007) Surface Wave front errors from OOF maps. Grayscale: +/- 2 rad Contour: ½ rad intervals Primary Surface Day: 300 micron Night: 250 micron Offset Pointing Day: 3.3 arcsec Night: 2.7 arcsec

10 R = (ηS P α β P ν γ ) (l eff l HA l z l tr l st ) (f oos f com f sg f tp )(t t t n t le t tb ) Scoring Equation Weather: Observing Efficiency Stringency Pressure Factors: Right Ascension Frequency Performance Limits: Observing Efficiency Hour Angle Zenith Angle Tracking Error Atmospheric Stability Other Factors: Observers on Site Completion of Projects Science Grades Thesis Projects Temporal Constraints: Transit Nighttime LST Exclusion Time Between

11 R = (ηS P α β P ν γ ) (l eff l HA l z l tr l st ) (f oos f com f sg f tp )(t t t n t le t tb ) Scoring Equation Weather: Observing Efficiency Stringency

12 Observing Efficiency

13

14 Stringency

15 R = (ηS P α β P ν γ ) (l eff l HA l z l tr l st ) (f oos f com f sg f tp )(t t t n t le t tb ) Scoring Equation Pressure Factors: Right Ascension Frequency

16 Pressure Factor

17 R = (ηS P α β P ν γ ) (l eff l HA l z l tr l st ) (f oos f com f sg f tp )(t t t n t le t tb ) Scoring Equation Performance Limits: Observing Efficiency Hour Angle Zenith Angle Tracking Error Atmospheric Stability

18 Observing Efficiency Limit

19 Hour Angle Limit Condon & Balser (2011)

20 Tracking Error Limit

21 Balser (2011); Mason & Perera (2010) Atmospheric Stability Limit

22 Packing (Open Sessions) Problem: a thief with a bag of capacity N, faced with a number (M) of possible goodies each having a different weight (cost) and value, how do you pack your bag to maximize your take? Brute Force: order (M!) Knapsack Algorithm: order (M*N) N = number of quarter hours to schedule M = number of potential sessions Overhead = 15 min. Sessoms

23 Scheduler Page

24 Schedule

25 User Home Page

26 User Project Page

27 Fini


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