Applying Databases for Data Visualization in Enhanced Follow-Up Regime of Baikal-GVD Neurtino Telescope

Abstract

This contribution describes improvements to the Baikal-GVD alert system related to alert visualization which could clarify the alert data and its possible relation to astrophysical phenomena. The Baikal-GVD online data processing and alert system was launched at the beginning of 2021. It is designed for fast online neutrino event reconstruction and, when a potential signal from an astrophysical source is detected, sending an alert message to collaboration members. It also searches for coincidences between internal alerts and other astrophysical experiment alerts. The databases schema used to store the alert data (MariaDB, InfluxDB, MongoDB) is described. Automation of data analysis and visualization processes occurs using specialized Python libraries (Matplotlib, Astropy, etc.), which provide an API for that. The capabilities of the Grafana software system for storing visualized data with the ability to share are also explored. One of the main tasks of our alert system is switch to real-time mode with a low latency mode in signal reception both by the Baikal-GVD trigger and in receiving and responding to an external alert.