Encapsulating textual contents into a MOC data structure for advanced applications

Abstract

Context:

The Multi-Order Coverage map (MOC) is a widely adopted standard promoted by the International Virtual Observatory Alliance (IVOA) to support data sharing and interoperability within the Virtual Observatory (VO) ecosystem. This hierarchical data structure efficiently encodes and visualizes irregularly shaped regions of the sky, enabling applications such as cross-matching large astronomical catalogs, visualizing multi-wavelength and multi-messenger surveys, and facilitating collaborative research through seamless interoperability in big-data-driven exploration.

Aims:

This study aims to explore potential enhancements to the MOC data structure by encapsulating textual descriptions and semantic embeddings into sky regions. Specifically, we introduce “Textual MOCs”, in which textual content is encapsulated, and “Semantic MOCs” that transform textual content into semantic embeddings. These enhancements are designed to enable advanced operations such as similarity searches and complex queries and to integrate with generative artificial intelligence (GenAI) tools to improve context-aware interactions and response accuracy in astronomical data analysis, and support agent-based applications.

Method:

We experimented with Textual MOCs by annotating detailed descriptions directly into the MOC sky regions, enriching the maps with contextual information suitable for interactive learning tools. For Semantic MOCs, we converted the textual content into semantic embeddings, numerical representations capturing textual meanings in multidimensional spaces, and stored them in high-dimensional vector databases optimized for efficient retrieval.

Results:

The implementation of Textual MOCs enhances user engagement by providing meaningful descriptions within sky regions, facilitating the development of effective game-based learning. Semantic MOCs enable sophisticated query capabilities, such as similarity-based searches and context-aware data retrieval, enhancing astronomical data analyses. Integration with multimodal generative AI systems allows for more accurate and contextually relevant interactions supporting both spatial, semantic and visual operations for advancing astronomical data analysis capabilities. Through straightforward examples, we discuss the fundamentals of this new experimental implementation.