6 August 2014 Artificial intelligence for the CTA Observatory scheduler
Author Affiliations +
Abstract
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.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Josep Colomé, Pau Colomer, Jordi Campreciós, Thierry Coiffard, Emma de Oña, Giovanna Pedaletti, Diego F. Torres, Alvaro Garcia-Piquer, "Artificial intelligence for the CTA Observatory scheduler", Proc. SPIE 9149, Observatory Operations: Strategies, Processes, and Systems V, 91490H (6 August 2014); doi: 10.1117/12.2057090; https://doi.org/10.1117/12.2057090
PROCEEDINGS
15 PAGES


SHARE
RELATED CONTENT

The LSST operations simulator
Proceedings of SPIE (August 04 2014)
The ASTRI SST 2M prototype for the Cherenkov Telescope Array...
Proceedings of SPIE (September 21 2015)
VERITAS a next generation very high energy gamma ray...
Proceedings of SPIE (June 23 2006)
SOAR control systems operation: OCS and TCS
Proceedings of SPIE (December 13 2002)
Scheduling simulation in ALMA
Proceedings of SPIE (September 16 2004)

Back to Top