The Online Observation Quality System for the ASTRI Mini-Array

TL;DR

The paper presents the design and implementation of the Online Observation Quality System (OOQS) for the ASTRI Mini-Array, enabling real-time data quality analysis and feedback during gamma-ray and interferometry observations with nine telescopes.

Contribution

It introduces a novel online data quality system that manages real-time analysis for multiple telescopes in a high-throughput astrophysical array.

Findings

Real-time data quality analysis performed successfully.

Alarms generated for critical conditions.

Data stored for further investigation.

Abstract

The ASTRI Mini-Array is an international collaboration led by the Italian National Institute for Astrophysics (INAF), aiming to construct and operate an array of nine Imaging Atmospheric Cherenkov Telescopes (IACTs) to study gamma-ray sources at very high energy (TeV) and to perform stellar intensity interferometry observations. This contribution describes the design and the technologies used by the ASTRI team to implement the Online Observation Quality System (OOQS). The main objective of the OOQS is to perform data quality analyses in real-time during Cherenkov and intensity interferometry observations to provide feedback to both the Central Control System and the Operator. The OOQS performs the analysis of key data quality parameters and can generate alarms to other sub-systems for a fast reaction to solve critical conditions. The results from the data quality analyses are saved into the Quality Archive for further investigations. The Operator can visualise the OOQS results through the Operator Human Machine Interface as soon as they are produced. The main challenge addressed by the OOQS design is to perform online data quality checks on the data streams produced by nine telescopes, acquired by the Array Data Acquisition System and forwarded to the OOQS. In the current OOQS design, the Redis in-memory database manages the data throughput generated by the telescopes, and the Slurm workload scheduler executes in parallel the high number of data quality analyses.