Speaker
Description
This contribution presents the architecture and implementation of an intelligent database system for astronomical alerts produced by the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). The system is designed to support efficient exploration of large-scale alert streams through both traditional query mechanisms and advanced similarity-based navigation.
Beyond standard attribute-based searches, the database enables fuzzy, proximity, and vector-based searches, allowing users to identify alerts with similar characteristics and to navigate the alert space in a recommendation-style manner. This is achieved by combining vector database techniques with additional domain-specific intelligence, providing flexible and extensible support for heterogeneous alert representations and evolving data formats.
The presentation will describe the overall system architecture, its theoretical foundations, and its implementation as a multi-language, multi-database solution that integrates multiple database technologies, including relational (SQL), document-oriented and key–value (NoSQL), and graph databases, each used according to its strengths and access patterns, and optimized for high-volume, high-velocity alert data. Particular attention will be given to data format flexibility, interoperability, and scalability. An intuitive, interactive web-service-based user interface allows both exploratory and programmatic access to the data.
Several advanced usage patterns, including similarity search, alert clustering, and exploratory classification workflows, will be demonstrated. The described system is an integral component of the Fink alert broker and contributes to efficient real-time and offline analysis of time-domain astronomical events.