1. LSST Scheduler Development Workshop Scheduler Design Francisco Delgado Sr. Software Engineer Telescope & Site

2. Scheduling the LSST Survey LSST as a robotic observatory Survey is automatic Multiple science goals Combine area distribution with temporal sampling Dynamic adaptation to weather Flexibility for survey adjustments during operations Flexibility for changes in science programs LSST as a robotic observatory Survey is automatic Multiple science goals Combine area distribution with temporal sampling Dynamic adaptation to weather Flexibility for survey adjustments during operations Flexibility for changes in science programs Scheduler Design 2Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

3. Scheduler concepts Sky field map, tiling regions, a target is a field/filter combination. Fully configurable set of concurrent competing science programs. Sky brightness dynamically modeled for each sky field with look- ahead window. Comprehensive observatory kinematic model for slew time optimizations. Target score balances science value and time cost Sky field map, tiling regions, a target is a field/filter combination. Fully configurable set of concurrent competing science programs. Sky brightness dynamically modeled for each sky field with look- ahead window. Comprehensive observatory kinematic model for slew time optimizations. Target score balances science value and time cost Scheduler Design 3Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

4. LSST Control Hierarchy DDS publish/subscribe Topics for Commands, Telemetry and Events Scheduler Design 4Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

5. Observatory Control System Scheduler Design 5Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

6. Scheduler Internal Block Diagram Conductor Optimizer Slew Time Scheduling Data Candidates Observation History Scheduler Control Telemetry History Image Quality Visits Targets Sched Telem Sched Mode Downtime Degraded Targets Observatory conditions Environment conditions Forecast Quality parameters Past observations Current observation Sched Telem Astronomical Sky Sky brightness coordinates Science Programs Value functions Cost functions Time Observatory Model Kinematic model coordinates Candidates Environment conditions Sched Config Scheduler Design 6Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

7. Operations Simulator System simulation and prototype for the Scheduler Validate observatory design Design science programs to achieve SRD Develop an efficient LSST scheduling strategy Systems engineering trade off studies Support Commissioning and Operations System simulation and prototype for the Scheduler Validate observatory design Design science programs to achieve SRD Develop an efficient LSST scheduling strategy Systems engineering trade off studies Support Commissioning and Operations Scheduler Design 7Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

8. Scheduler Design 8Scheduler Development Workshop Tucson, Arizona March 18-19, 2015 OpSim requirements Simulate Operations visit by visit for 10 years Simulate Observatory (Telescope & Camera kinematics, slew & track) Simulate Environment (clouds, seeing, sky brightness) Prototype Scheduler (targets generation and scheduling algorithms) Set of proposals, SRD defined universal plus auxiliary projects Flexibility for algorithm experimentation Simulate Operations visit by visit for 10 years Simulate Observatory (Telescope & Camera kinematics, slew & track) Simulate Environment (clouds, seeing, sky brightness) Prototype Scheduler (targets generation and scheduling algorithms) Set of proposals, SRD defined universal plus auxiliary projects Flexibility for algorithm experimentation

9. OpSim implementation Python language for the logic and data handling C++ for libraries 20k lines of code approx. MySQL database with 22 tables for the history of visits, slews and sequences, sky conditions, etc. Typical 10 year run takes 50 hours in personal computers Time dominated by DB access & Slew time calculations 4~5 Gb in Database for a 10 year run Memory dominated by deterministic look-ahead data, 4Gb per lunation with 5 mins granularity Python language for the logic and data handling C++ for libraries 20k lines of code approx. MySQL database with 22 tables for the history of visits, slews and sequences, sky conditions, etc. Typical 10 year run takes 50 hours in personal computers Time dominated by DB access & Slew time calculations 4~5 Gb in Database for a 10 year run Memory dominated by deterministic look-ahead data, 4Gb per lunation with 5 mins granularity Scheduler Design 9Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

10. OpSim Architecture Scheduler Design 10Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

11. Science Programs parameters Sky region. Number of visits per field in each filter. Cadence constraints for revisits or sequences. Airmass limits. Sky brightness constraints. Seeing requirements. Activation times. Sky region. Number of visits per field in each filter. Cadence constraints for revisits or sequences. Airmass limits. Sky brightness constraints. Seeing requirements. Activation times. Scheduler Design 11Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

12. Scheduler Design 12Scheduler Development Workshop Tucson, Arizona March 18-19, 2015 Science Programs classes  Area distribution programs  Designed to obtain uniform distribution  Basic parameter: goal visits per filter  Look-ahead info: future available time for the targets  Time distribution programs  Designed to obtain specified intervals in sequences  Basic parameter: time window for visits interval  Look-ahead info: visibility for next intervals  Area distribution programs  Designed to obtain uniform distribution  Basic parameter: goal visits per filter  Look-ahead info: future available time for the targets  Time distribution programs  Designed to obtain specified intervals in sequences  Basic parameter: time window for visits interval  Look-ahead info: visibility for next intervals

13. Selecting the next visit  Dynamic and adaptive process for each visit:  Each science program:  analyzes its assigned sky region and selects the candidate targets (field/filter) that comply with its requirements for airmass, sky-brightness and seeing.  computes the science value for each selected target according to its own distribution and cadence requirements.  The conductor optimizer combines the targets from all the science programs and using the observatory model incorporates the slew cost to obtain an overall rank.  The target with the highest rank is selected.  Dynamic and adaptive process for each visit:  Each science program:  analyzes its assigned sky region and selects the candidate targets (field/filter) that comply with its requirements for airmass, sky-brightness and seeing.  computes the science value for each selected target according to its own distribution and cadence requirements.  The conductor optimizer combines the targets from all the science programs and using the observatory model incorporates the slew cost to obtain an overall rank.  The target with the highest rank is selected. Scheduler Design 13Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

14. Area distribution programs –Designed to obtain uniform distribution –Basic parameter: goal visits per filter –Field-filters receiving visits reduce their rank, while not observed Field-filters increase their rank. Scheduler Design 14Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

15. Time distribution programs –Designed to obtain specified intervals –Basic parameter: time window for visits interval –Each field has a sequence of visits with time intervals. –This rank envelope promotes visits as close to the desired intervals as target competition allows Scheduler Design 15Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

16. Sequence Possibilities  One single sequence per field  Multiple subsequences per field, different filters  Option for collecting pairs of visits in any subsequence  Option for combining area with time distribution  Option for collecting deep drilling sequences, back-to-back visits changing filters  Option for nested subsequences Scheduler Design 16Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

17. Look-ahead  A time window is defined for a number of nights to the future.  For each target from the candidates list:  Alt-Az-Pa, airmass and sky-brightness are pre-calculated.  Visibility is determined from each science program constraints.  Science programs have this look-ahead information for improving time distribution and efficiency in sequences.  A time window is defined for a number of nights to the future.  For each target from the candidates list:  Alt-Az-Pa, airmass and sky-brightness are pre-calculated.  Visibility is determined from each science program constraints.  Science programs have this look-ahead information for improving time distribution and efficiency in sequences. Scheduler Design 17Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

18. Area distribution with look-ahead –availableTime is the addition of the future time windows when the target (field-filter) is visible for the science program. –targetMerit gives a normalized range of values –These example equations balance the area distribution taking into account the future availability of the field-filter Scheduler Design 18Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

19. Sequences filtering with look ahead –A science program with sequences evaluates the look ahead visibility of the field-filter series of visits given a start time. –A list of possible start times is populated for each sequence. –The goal is to start only feasible sequences increasing the efficiency Scheduler Design 19Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

20. Science Proposals balance –This equations promote a balanced progress in the competing science proposals Scheduler Design 20Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

21. Scheduler Design 21Scheduler Development Workshop Tucson, Arizona March 18-19, 2015 Scheduler Telemetry History Control Targets Image Quality Scheduler Interfaces in OCS OCS Application communications middleware TCS EFD DMCS OCS Sequencer Visits Sched Telem CCS Cmd Visits

22. Scheduler Design 22Scheduler Development Workshop Tucson, Arizona March 18-19, 2015 Scheduler Telemetry History Control Targets Image Quality Scheduler Interfaces in OBSIM OBSIM Simulation kernel OBSIM Simulation kernel communications middleware OBSIM Observatory Model OBSIM Observatory Model OBSIM Survey Database OBSIM Survey Database OBSIM Simulation kernel OBSIM Simulation kernel Visits Sched Telem Cmd Visits OBSIM Environment Model OBSIM Environment Model OBSIM Quality Simulator OBSIM Quality Simulator

23. ObSim Internal Block Diagram Simulation Kernel Survey Database ObSim Control Telemetry History Image Quality Visits Targets Sched Telem Sched Mode Downtime Degraded Targets Observatory conditions Environment conditions Forecast Quality parameters Past observations Current observation Sched Telem Quality Simulator Downtime Model Time Sequencer Observatory Model Kinematic model coordinates Environment Model Sky brightness coordinates Time Config Sched Config Scheduler Design 23Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

24. Scheduler Internal Block Diagram Conductor Optimizer Slew Time Scheduling Data Candidates Observation History Scheduler Control Telemetry History Image Quality Visits Targets Sched Telem Sched Mode Downtime Degraded Targets Observatory conditions Environment conditions Forecast Quality parameters Past observations Current observation Sched Telem Astronomical Sky Sky brightness coordinates Science Programs Value functions Cost functions Time Observatory Model Kinematic model coordinates Candidates Environment conditions Sched Config Scheduler Design 24Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

25. Scheduler Inputs/Outputs  Inputs  Control  Scheduler Configuration  Scheduler Mode  Downtime  Degraded  Telemetry  Observatory conditions  Environment conditions  Forecast  Image Quality  DM feedback  History  Past observations  Visits  Current observation  Outputs  Targets  Scheduling telemetry  Inputs  Control  Scheduler Configuration  Scheduler Mode  Downtime  Degraded  Telemetry  Observatory conditions  Environment conditions  Forecast  Image Quality  DM feedback  History  Past observations  Visits  Current observation  Outputs  Targets  Scheduling telemetry Scheduler Design 25Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

26. Summary  Scheduler design integrated with OCS architecture.  OCS telemetry architecture enables the use of any variable for scheduling purposes.  Partition and architecture makes for a flexible implementation.  Designed to allow a distributed deployment.  Scheduling strategies have been extensively tested in OpSim.  Simple scheduling algorithms applied to thousands of competing targets produce emerging behavior to solve a complex problem.  Scheduler design integrated with OCS architecture.  OCS telemetry architecture enables the use of any variable for scheduling purposes.  Partition and architecture makes for a flexible implementation.  Designed to allow a distributed deployment.  Scheduling strategies have been extensively tested in OpSim.  Simple scheduling algorithms applied to thousands of competing targets produce emerging behavior to solve a complex problem. Scheduler Design 26Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

27. End of Presentation

28. Backup slides Scheduler Design 28Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

29. LSST Introduction The Large Synoptic Survey Telescope is a complex hardware – software system of systems, making up a highly automated observatory in the form of an 8.4m wide-field telescope, a 3.2 billion pixel camera, and a peta-scale data processing and archiving system. The survey consists of a continuous cadence of visits covering the entire observable sky in 6 different colors with different specifications for depth and time intervals for multiple science programs.

30. DDS COMMUNICATIONS MIDDLEWARE (Commands, Telemetry, Events) OCS Applicati on CCS Interface OCS Remote OCS Sequenc er OCS Mainten. OCS Telemetr y OCS Monitor OCS Operator OCS Schedule r DMCS Interface TCS Enviro n. Contro ller TCS Enclos ure Contro ller TCS Rot/He x Contro ller TCS Mount Contro ller TCS M2 Contro ller TCS M1M3 Contro ller TCS Optics Contro ller Calibrat ion TCS Operat or TCS Pointin g Kernel TCS Appl. TCS Wavefro nt Interface MUX DEMUX MUX DEMUX Camera Guider Interface ILC Networ k Temper. ILC Networ k Devic e Contr ol Devic e Contr ol Devic e Contr ol Devic e Contr ol ILC Networ k Surface Science Data Interface : : DDS Communications NON DDS Communications Auxiliary Telescop e OCS Engineerin g Facility DB Alignm ent Auxilia ry Equip ment -Distributed Control System -Scalable Architecture -Loosely-coupled systems -Interfaces defined by the information model -Connectivity complexity managed by the data bus LSST CONTROL ARCHITECTURE Scheduler Design 30Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

31. OCS communications Scheduler Design 31Scheduler Development Workshop Tucson, Arizona March 18-19, 2015 DDS COMMUNICATIONS MIDDLEWARE (Commands, Telemetry, Events) OCS Application OCS Remote OCS Sequencer OCS Maintenance OCS Telemetry OCS Monitor OCS Operator OCS Scheduler OCS Scheduler

32. Scheduler Design 32Scheduler Development Workshop Tucson, Arizona March 18-19, 2015 Simulation Operations Scheduler Telescope Telemetry Weather Telemetry Downtime Status Telescope Model Weather Model Downtime Model Observatory Database Survey Database Observatory Control System (OCS) Scheduler Selected Targets Simulation Kernel Telemetry Environmental Conditions Observed Targets Control Database Simulation Params Scheduling Params Telemetry Environmental Conditions Observed Targets Control Selected Targets Database OpSim includes Scheduler prototype

33. Scheduler Composition Scheduler Design 33Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

34. Model Based Systems Engineering (MBSE) The LSST uses MBSE to capture the high level system development The language is SysML The tool is Enterprise Architect The model captures and relates: – Requirements – Interfaces – Overall System Architecture – Components Structure – System Behavior – Operational Definitions Document 9336 “Using SysML for MBSE Analysis of the LSST System

35. Requirements Flow down Science Requirements Document LPM-17 Science Requirements Document LPM-17 Scheduler Requirements LSE-190 Scheduler Requirements LSE-190 Observatory System Specifications LSE-30 Observatory System Specifications LSE-30 LSST System Requirements LSE-29 LSST System Requirements LSE-29 OpSim Requirements LSE-189 OpSim Requirements LSE-189 Observatory Control System Requirements LSE-62 Observatory Control System Requirements LSE-62 Science Book Metrics Requirements DOC-15319 Metrics Requirements DOC-15319 Science Collaborations Scheduler Design 35Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

36. Model-Based Systems Engineering Scheduler Design 36Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

37. OCS requirements flow-down Science Requirements Document is the parent for all requirements flow down. LPM-17 LSST System Requirements (high level what the LSST is and must do) LSE-29 Observatory System Specifications (high level how the LSST will do what it must) LSE-30 Observatory Control System Requirements (Subsystem Requirements) LSE-62

38. Scheduler Architecture Scheduler Design 38Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

39. Summary  Powerful tool for designing the survey and systems engineering  OpSim was key on site-selection, validation of telescope-camera specifications, and demonstrated that the science requirements could be met.  OpSim-Scheduler as a prototype for OCS-Scheduler.  OpSim as a simulation environment for the Scheduler prototype.  OpSim will be evolved into an operational tool for survey assessment and planning.  New look-ahead capabilities and scheduling algorithms in development.  Powerful tool for designing the survey and systems engineering  OpSim was key on site-selection, validation of telescope-camera specifications, and demonstrated that the science requirements could be met.  OpSim-Scheduler as a prototype for OCS-Scheduler.  OpSim as a simulation environment for the Scheduler prototype.  OpSim will be evolved into an operational tool for survey assessment and planning.  New look-ahead capabilities and scheduling algorithms in development. Scheduler Design 39Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

40. Operations Simulator Verify the specifications of LSST hardware and survey against SRD Experiment with sets of science programs Experiment scheduling algorithms and strategies Systems engineering trade off studies Refine requirements for OCS Scheduler

41. Operations Simulator Software package for simulating the 10 years survey in a visit by visit, slew by slew detail. Detailed kinematic model of the telescope+camera+dome Sophisticated sky model, calculating sky brightness using the Krisciunas and Schaeffer model. It tracks the sun and moon using SLALIB routines. Actual seeing and clouds historic tables from the site. Multiple science programs that implement a cadence that satisfies the science requirements.

42. Operations Simulator Requirements Simulate Operations Simulate Observatory (Telescope & Camera kinematics, slew & track) Simulate Environment (clouds, seeing, sky brightness) Prototype Scheduler (targets generation and scheduling algorithms) Set of proposals, SRD defined universal plus key projects Flexibility for algorithm experimentation

43. OpSim requirements in SysML

44. OpSim components

45. OpSim activities

46. Scheduler Target List Scheduler Design 46Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

47. OpSim: start night

48. Scheduler: update target list

49. OpSim Telescope model parameters # speed in degrees/second # acceleration in degrees/second**2 DomAlt_MaxSpeed = 1.75 DomAlt_Accel = 0.875 DomAlt_Decel = 0.875 DomAz_MaxSpeed = 1.5 DomAz_Accel = 0.75 DomAz_Decel = 0.75 TelAlt_MaxSpeed = 3.5 TelAlt_Accel = 3.5 TelAlt_Decel = 3.5 TelAz_MaxSpeed = 7.0 TelAz_Accel = 7.0 TelAz_Decel = 7.0 # not used in slew calculation Rotator_MaxSpeed = 3.5 Rotator_Accel = 1.0 Rotator_Decel = 1.0 # absolute position limits due to cable wrap # the range [0 360] must be included TelAz_MinPos = -270.0 TelAz_MaxPos = 270.0 Rotator_MinPos = -90.0 Rotator_MaxPos = 90.0 Rotator_FollowSky = False # Times in sec Filter_MoveTime = 120.0 Settle_Time = 3.0 # In azimuth only DomSettle_Time = 1.0 Readout_Time = 2.0 # Delay factor for Open Loop optics correction, # in units of seconds/(degrees in ALT slew) TelOpticsOL_Slope = 1.0/3.5 # Table of delay factors for Closed Loop optics correction # according to the ALT slew range. # _AltLimit is the Altitude upper limit in degrees of a range. # The lower limit is the upper limit of the previous range. # The lower limit for the first range is 0 # _Delay is the time delay in seconds for the corresponding range. TelOpticsCL_Delay = 0.0 TelOpticsCL_AltLimit = 9.0 # 0 delay due to CL up to 9 degrees in ALT slew TelOpticsCL_Delay = 20.0 TelOpticsCL_AltLimit = 90.0 Scheduler Design 49Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

50. Detailed slew simulation Session ID: 271number of nights: 365number of exposures: 173999 exposures/night: 476.7 average slew time: 9.79s statistics for angle TelAlt: min= 15.1d max= 86.5d avg= 54.9d std= 14.2d statistics for angle TelAz: min=-270.0d max= 270.0d avg= -19.0d std= 99.8d statistics for angle RotPos: min= -90.0d max= 90.0d avg= -9.4d std= 52.1d slew activity for DomAlt: active= 90.5% of slews, active avg= 3.47s, total avg= 3.14s, max= 22.05s, in critical path= 0.0% with avg= 0.00s cont= 0.00s slew activity for DomAz: active= 90.5% of slews, active avg= 5.55s, total avg= 5.02s, max=106.25s, in critical path= 0.8% with avg= 83.63s cont= 0.64s slew activity for TelAlt: active= 90.5% of slews, active avg= 3.47s, total avg= 3.14s, max= 22.05s, in critical path= 38.2% with avg= 3.69s cont= 1.41s slew activity for TelAz: active= 90.5% of slews, active avg= 4.87s, total avg= 4.41s, max=105.94s, in critical path= 45.3% with avg= 5.83s cont= 2.64s slew activity for Rotator: active= 90.5% of slews, active avg= 4.68s, total avg= 4.23s, max= 54.81s, in critical path= 3.9% with avg= 16.18s cont= 0.63s slew activity for Filter: active= 2.2% of slews, active avg=120.00s, total avg= 2.67s, max=120.00s, in critical path= 2.2% with avg=120.00s cont= 2.67s slew activity for TelOpticsOL: active= 90.5% of slews, active avg= 0.99s, total avg= 0.89s, max= 18.55s, in critical path= 16.9% with avg= 1.89s cont= 0.32s slew activity for Readout: active= 99.7% of slews, active avg= 1.00s, total avg= 1.00s, max= 1.00s, in critical path= 0.0% with avg= 0.00s cont= 0.00s slew activity for Settle: active= 99.7% of slews, active avg= 1.00s, total avg= 1.00s, max= 1.00s, in critical path= 75.9% with avg= 1.00s cont= 0.76s slew activity for TelOpticsCL: active= 3.1% of slews, active avg= 22.87s, total avg= 0.71s, max= 40.00s, in critical path= 3.1% with avg= 22.87s cont= 0.71s Session ID: 271number of nights: 365number of exposures: 173999 exposures/night: 476.7 average slew time: 9.79s statistics for angle TelAlt: min= 15.1d max= 86.5d avg= 54.9d std= 14.2d statistics for angle TelAz: min=-270.0d max= 270.0d avg= -19.0d std= 99.8d statistics for angle RotPos: min= -90.0d max= 90.0d avg= -9.4d std= 52.1d slew activity for DomAlt: active= 90.5% of slews, active avg= 3.47s, total avg= 3.14s, max= 22.05s, in critical path= 0.0% with avg= 0.00s cont= 0.00s slew activity for DomAz: active= 90.5% of slews, active avg= 5.55s, total avg= 5.02s, max=106.25s, in critical path= 0.8% with avg= 83.63s cont= 0.64s slew activity for TelAlt: active= 90.5% of slews, active avg= 3.47s, total avg= 3.14s, max= 22.05s, in critical path= 38.2% with avg= 3.69s cont= 1.41s slew activity for TelAz: active= 90.5% of slews, active avg= 4.87s, total avg= 4.41s, max=105.94s, in critical path= 45.3% with avg= 5.83s cont= 2.64s slew activity for Rotator: active= 90.5% of slews, active avg= 4.68s, total avg= 4.23s, max= 54.81s, in critical path= 3.9% with avg= 16.18s cont= 0.63s slew activity for Filter: active= 2.2% of slews, active avg=120.00s, total avg= 2.67s, max=120.00s, in critical path= 2.2% with avg=120.00s cont= 2.67s slew activity for TelOpticsOL: active= 90.5% of slews, active avg= 0.99s, total avg= 0.89s, max= 18.55s, in critical path= 16.9% with avg= 1.89s cont= 0.32s slew activity for Readout: active= 99.7% of slews, active avg= 1.00s, total avg= 1.00s, max= 1.00s, in critical path= 0.0% with avg= 0.00s cont= 0.00s slew activity for Settle: active= 99.7% of slews, active avg= 1.00s, total avg= 1.00s, max= 1.00s, in critical path= 75.9% with avg= 1.00s cont= 0.76s slew activity for TelOpticsCL: active= 3.1% of slews, active avg= 22.87s, total avg= 0.71s, max= 40.00s, in critical path= 3.1% with avg= 22.87s cont= 0.71s Scheduler Design 50Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

51. Survey database analysis Simulation Survey Tools for Analysis and Reporting (SSTAR). Automatic analysis from the output DB. Statistics, charts and metrics. Scheduler Design 51Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

52. Filter Map Scheduler Design 52Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

53. Joint completeness comparison Scheduler Design 53Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

54. Organization Telescope & Site Systems Engineering Simulation Systems Engineering Simulation Scheduler Team Engineering Lead SW Scheduler Scientist SW SE Science Lead Simulation Runs Scheduler Design 54Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

55. Design Validation using MBSE The following slides show an example of the triad validation methodology for the OCS design. From LSST Observatory all the way to the OCS Scheduler Kinematic model structure component, flowing top down through the corresponding requirements and behavior.

56. Scheduler structure traceability to requirements

57. OCS Requirements Organization

58. OCS Requirements Context

59. OCS Scheduler Requirements

60. OpSim structure traceability to requirements

61. Perform Survey Activity

62. Perform Science Observations Validation Scheduler Design 62Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

63. Select Target Validation Scheduler Design 63Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

64. Rank Targets validation Scheduler Design 64Scheduler Development Workshop Tucson, Arizona March 18-19, 2015

65. Summary Powerful tool for survey designing and systems engineering OpSim was key on site-selection, telescope-camera specifications validation, and finding a survey that fulfilled the science requirements. OpSim-Scheduler as a prototype for OCS-Scheduler, reducing the risk on a critical component. OpSim can be evolved into an operational tool for survey assessment and planning. OpSim as a simulation environment for the Scheduler prototype Scheduler arquitecture designed for flexibility and multiple goals Scheduler Design 65Scheduler Development Workshop Tucson, Arizona March 18-19, 2015