TY - GEN
T1 - Artificial intelligence for the CTA Observatory scheduler
AU - Colomé, Josep
AU - Colomer, Pau
AU - Campreciós, Jordi
AU - Coiffard, Thierry
AU - De Oña, Emma
AU - Pedaletti, Giovanna
AU - Torres, Diego F.
AU - Garcia-Piquer, Alvaro
N1 - Publisher Copyright:
© 2014 SPIE.
PY - 2014
Y1 - 2014
N2 - The Cherenkov Telescope Array (CTA) project will be the next generation ground-based very high energy gamma-ray instrument. The success of the precursor projects (i.e., HESS, MAGIC, VERITAS) motivated the construction of this large infrastructure that is included in the roadmap of the ESFRI projects since 2008. CTA is planned to start the construction phase in 2015 and will consist of two arrays of Cherenkov telescopes operated as a proposal-driven open observatory. Two sites are foreseen at the southern and northern hemispheres. The CTA observatory will handle several observation modes and will have to operate tens of telescopes with a highly efficient and reliable control. Thus, the CTA planning tool is a key element in the control layer for the optimization of the observatory time. The main purpose of the scheduler for CTA is the allocation of multiple tasks to one single array or to multiple sub-arrays of telescopes, while maximizing the scientific return of the facility and minimizing the operational costs. The scheduler considers long- and short-term varying conditions to optimize the prioritization of tasks. A short-term scheduler provides the system with the capability to adapt, in almost real-time, the selected task to the varying execution constraints (i.e., Targets of Opportunity, health or status of the system components, environment conditions). The scheduling procedure ensures that long-term planning decisions are correctly transferred to the short-term prioritization process for a suitable selection of the next task to execute on the array. In this contribution we present the constraints to CTA task scheduling that helped classifying it as a Flexible Job-Shop Problem case and finding its optimal solution based on Artificial Intelligence techniques. We describe the scheduler prototype that uses a Guarded Discrete Stochastic Neural Network (GDSN), for an easy representation of the possible long- and short-term planning solutions, and Constraint Propagation techniques. A simulation platform, an analysis tool and different test case scenarios for CTA were developed to test the performance of the scheduler and are also described.
AB - The Cherenkov Telescope Array (CTA) project will be the next generation ground-based very high energy gamma-ray instrument. The success of the precursor projects (i.e., HESS, MAGIC, VERITAS) motivated the construction of this large infrastructure that is included in the roadmap of the ESFRI projects since 2008. CTA is planned to start the construction phase in 2015 and will consist of two arrays of Cherenkov telescopes operated as a proposal-driven open observatory. Two sites are foreseen at the southern and northern hemispheres. The CTA observatory will handle several observation modes and will have to operate tens of telescopes with a highly efficient and reliable control. Thus, the CTA planning tool is a key element in the control layer for the optimization of the observatory time. The main purpose of the scheduler for CTA is the allocation of multiple tasks to one single array or to multiple sub-arrays of telescopes, while maximizing the scientific return of the facility and minimizing the operational costs. The scheduler considers long- and short-term varying conditions to optimize the prioritization of tasks. A short-term scheduler provides the system with the capability to adapt, in almost real-time, the selected task to the varying execution constraints (i.e., Targets of Opportunity, health or status of the system components, environment conditions). The scheduling procedure ensures that long-term planning decisions are correctly transferred to the short-term prioritization process for a suitable selection of the next task to execute on the array. In this contribution we present the constraints to CTA task scheduling that helped classifying it as a Flexible Job-Shop Problem case and finding its optimal solution based on Artificial Intelligence techniques. We describe the scheduler prototype that uses a Guarded Discrete Stochastic Neural Network (GDSN), for an easy representation of the possible long- and short-term planning solutions, and Constraint Propagation techniques. A simulation platform, an analysis tool and different test case scenarios for CTA were developed to test the performance of the scheduler and are also described.
KW - Artificial Intelligence
KW - Cherenkov Telescope Array
KW - Observation scheduling
KW - Observatory operations
KW - Performance analysis and metrics
KW - Scheduling simulation
KW - Telescopes
UR - http://www.scopus.com/inward/record.url?scp=84922647461&partnerID=8YFLogxK
U2 - 10.1117/12.2057090
DO - 10.1117/12.2057090
M3 - Conference contribution
AN - SCOPUS:84922647461
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Observatory Operations
A2 - Peck, Alison B.
A2 - Benn, Chris R.
A2 - Seaman, Robert L.
PB - SPIE
T2 - Observatory Operations: Strategies, Processes, and Systems V
Y2 - 25 June 2014 through 27 June 2014
ER -