TSCat: data model and storage engine for AI-based light-curve analysis

Abstract

Light curve data are one of the most important data sources in time domain astronomy research. With the advancement of observation facilities and the continuous accumulation of observation data, and considering the analysis needs of large sample data sets, software or tools based on new technologies, especially artificial intelligence (AI), will be indispensable for light curve analysis. The light curve analysis tool designed by an individual will follow its own defined data structure, which will make the tools designed and developed by different individuals incompatible. A unified light curve data model will be able to solve this compatibility problem, similar to the traditional flexible image transport system (FITS) file format. This paper proposes a light curve data model named TSCat, designs and implements a data storage engine as an example. The TSCat data model defines the basic metadata and format required for storing optical curve data following the international virtual observatory alliance (IVOA) data format specification system. The TSCat storage engine implements basic operations such as importing, storing, and accessing light curve data. The function and performance of the TSCat storage engine are evaluated through the actual observation data. The experimental results show that the TSCat data model is complete enough to support the analysis of light curves. TSCat will help support the standardization of the data access level of software and tools in the field of light curve analysis, and provide a new reference for the specification system of astronomical scientific data formats.