Deep Reinforcement Learning for Efficient Scheduling of Ground-based Astronomical Observations
Ground-based astronomical observations face inherent challenges from weather changes, target visibility window constraints, and observational requirements. Enhancing the efficiency and effectiveness of telescope operations has long been a key objective for many observatories because of the high cost...
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| Format: | Article |
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IOP Publishing
2025-01-01
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| Series: | The Astronomical Journal |
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| Online Access: | https://doi.org/10.3847/1538-3881/ade3dc |
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| author | Hai Cao Shaoming Hu Junju Du Xu Chen Shuqi Liu Shuai Feng Bo Zhang Yuchen Jiang |
| author_facet | Hai Cao Shaoming Hu Junju Du Xu Chen Shuqi Liu Shuai Feng Bo Zhang Yuchen Jiang |
| author_sort | Hai Cao |
| collection | DOAJ |
| description | Ground-based astronomical observations face inherent challenges from weather changes, target visibility window constraints, and observational requirements. Enhancing the efficiency and effectiveness of telescope operations has long been a key objective for many observatories because of the high cost of observational resources. In this study, we formalize observation scheduling as a time-dependent combinatorial optimization problem. To achieve this, we implement a pointer network with temporal attention that is capable of planning observations while accounting for time-varying factors such as moonlight interference, target altitude, and air mass, which impact the exposure time and image quality. To support the training of the deep neural network, we propose a scoring mechanism to evaluate the effectiveness of the observations, which is optimized through a refined REINFORCE algorithm with a baseline. Furthermore, an exposure time calculator and an equipment kinematic model are incorporated to dynamically estimate the time costs during the decision-making process. The simulation results demonstrated that the trained model significantly outperformed both manual scheduling and a greedy algorithm in terms of theoretical reward scores and the total number of scheduled targets. Observation experiments conducted using a dual-telescope system at Muztaga observatory further validated the superiority of our approach, demonstrating a 45.8% enhancement in the total signal-to-noise ratio across all observed targets and a 24.1% increase in the number of completed tasks under the same observing conditions. |
| format | Article |
| id | doaj-art-a6c80bc036284d639015e66db5890512 |
| institution | DOAJ |
| issn | 1538-3881 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astronomical Journal |
| spelling | doaj-art-a6c80bc036284d639015e66db58905122025-08-20T03:11:52ZengIOP PublishingThe Astronomical Journal1538-38812025-01-0117028810.3847/1538-3881/ade3dcDeep Reinforcement Learning for Efficient Scheduling of Ground-based Astronomical ObservationsHai Cao0https://orcid.org/0000-0001-9578-6613Shaoming Hu1https://orcid.org/0000-0003-3217-7794Junju Du2https://orcid.org/0000-0002-8571-8235Xu Chen3https://orcid.org/0000-0001-5603-7521Shuqi Liu4Shuai Feng5https://orcid.org/0000-0001-7288-7251Bo Zhang6Yuchen Jiang7Shandong Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, School of Space Science and Technology, Institute of Space Sciences, Shandong University , Weihai, Shandong 264209, People’s Republic of China ; husm@sdu.edu.cn; School of Mechanical, Electrical and Information Engineering, Shandong University , Weihai, Shandong 264209, People’s Republic of ChinaShandong Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, School of Space Science and Technology, Institute of Space Sciences, Shandong University , Weihai, Shandong 264209, People’s Republic of China ; husm@sdu.edu.cnShandong Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, School of Space Science and Technology, Institute of Space Sciences, Shandong University , Weihai, Shandong 264209, People’s Republic of China ; husm@sdu.edu.cnShandong Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, School of Space Science and Technology, Institute of Space Sciences, Shandong University , Weihai, Shandong 264209, People’s Republic of China ; husm@sdu.edu.cnShandong Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, School of Space Science and Technology, Institute of Space Sciences, Shandong University , Weihai, Shandong 264209, People’s Republic of China ; husm@sdu.edu.cnShandong Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, School of Space Science and Technology, Institute of Space Sciences, Shandong University , Weihai, Shandong 264209, People’s Republic of China ; husm@sdu.edu.cnShandong Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, School of Space Science and Technology, Institute of Space Sciences, Shandong University , Weihai, Shandong 264209, People’s Republic of China ; husm@sdu.edu.cnShandong Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, School of Space Science and Technology, Institute of Space Sciences, Shandong University , Weihai, Shandong 264209, People’s Republic of China ; husm@sdu.edu.cnGround-based astronomical observations face inherent challenges from weather changes, target visibility window constraints, and observational requirements. Enhancing the efficiency and effectiveness of telescope operations has long been a key objective for many observatories because of the high cost of observational resources. In this study, we formalize observation scheduling as a time-dependent combinatorial optimization problem. To achieve this, we implement a pointer network with temporal attention that is capable of planning observations while accounting for time-varying factors such as moonlight interference, target altitude, and air mass, which impact the exposure time and image quality. To support the training of the deep neural network, we propose a scoring mechanism to evaluate the effectiveness of the observations, which is optimized through a refined REINFORCE algorithm with a baseline. Furthermore, an exposure time calculator and an equipment kinematic model are incorporated to dynamically estimate the time costs during the decision-making process. The simulation results demonstrated that the trained model significantly outperformed both manual scheduling and a greedy algorithm in terms of theoretical reward scores and the total number of scheduled targets. Observation experiments conducted using a dual-telescope system at Muztaga observatory further validated the superiority of our approach, demonstrating a 45.8% enhancement in the total signal-to-noise ratio across all observed targets and a 24.1% increase in the number of completed tasks under the same observing conditions.https://doi.org/10.3847/1538-3881/ade3dcGround-based astronomyObservational astronomyAstronomical techniquesNeural networksOptical observation |
| spellingShingle | Hai Cao Shaoming Hu Junju Du Xu Chen Shuqi Liu Shuai Feng Bo Zhang Yuchen Jiang Deep Reinforcement Learning for Efficient Scheduling of Ground-based Astronomical Observations The Astronomical Journal Ground-based astronomy Observational astronomy Astronomical techniques Neural networks Optical observation |
| title | Deep Reinforcement Learning for Efficient Scheduling of Ground-based Astronomical Observations |
| title_full | Deep Reinforcement Learning for Efficient Scheduling of Ground-based Astronomical Observations |
| title_fullStr | Deep Reinforcement Learning for Efficient Scheduling of Ground-based Astronomical Observations |
| title_full_unstemmed | Deep Reinforcement Learning for Efficient Scheduling of Ground-based Astronomical Observations |
| title_short | Deep Reinforcement Learning for Efficient Scheduling of Ground-based Astronomical Observations |
| title_sort | deep reinforcement learning for efficient scheduling of ground based astronomical observations |
| topic | Ground-based astronomy Observational astronomy Astronomical techniques Neural networks Optical observation |
| url | https://doi.org/10.3847/1538-3881/ade3dc |
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