The Online Observation Quality System Software Architecture for the ASTRI Mini-Array Project
| Autor: | Parmiggiani, N., Bulgarelli, A., Baroncelli, L., Addis, A., Fioretti, V., Di Piano, A., Capalbi, M., Catalano, O., Conforti, V., Fiori, M., Gianotti, F., Iovenitti, S., Lucarelli, F., Maccarone, M. C., Mineo, T., Lombardi, S., Pastore, V., Russo, F., Sangiorgi, P., Scuderi, S., Tosti, G., Trifoglio, M., Zampieri, L., Project, the ASTRI |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Proceedings Volume 12189, Software and Cyberinfrastructure for Astronomy VII; 121892H (2022) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1117/12.2629278 |
| Popis: | The ASTRI Mini-Array is an international collaboration led by the Italian National Institute for Astrophysics. This project aims to construct and operate an array of nine Imaging Atmospheric Cherenkov Telescopes to study gamma-ray sources at very high energy (TeV) and perform stellar intensity interferometry observations. We describe the software architecture and the technologies used to implement the Online Observation Quality System (OOQS) for the ASTRI Mini-Array project. The OOQS aims to execute data quality checks on the data acquired in real-time by the Cherenkov cameras and intensity interferometry instruments, and provides feedback to both the Central Control System and the Operator about abnormal conditions detected. The OOQS can notify other sub-systems, triggering their reaction to promptly correct anomalies. The results from the data quality analyses (e.g. camera plots, histograms, tables, and more) are stored in the Quality Archive for further investigation and they are summarised in reports available to the Operator. Once the OOQS results are stored, the operator can visualize them using the Human Machine Interface. The OOQS is designed to manage the high data rate generated by the instruments (up to 4.5 GB/s) and received from the Array Data Acquisition System through the Kafka service. The data are serialized and deserialized during the transmission using the Avro framework. The Slurm workload scheduler executes the analyses exploiting key features such as parallel analyses and scalability. Comment: 12 pages, 7 Figures. arXiv admin note: text overlap with arXiv:2108.04515 |
| Databáze: | arXiv |
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