Cross-matching operation, which is to find corresponding data for the same celestial object or region from multiple catalogues, is indispensable to astronomical data analysis and research. Due to the large amount of astronomical catalogues generated by the ongoing and next generation large-scale sky surveys, the time complexity of the cross-matching is increasing dramatically. Heterogeneous computing environments provide a theoretical possibility to accelerate the cross-matching, but the performance advantages of heterogeneous computing resources have not been fully utilized. To meet the challenge of cross-matching for substantial increasing amount of astronomical observation data, this paper proposes HLC2 (Heterogeneous-computing-enabled Large Catalogue Cross-matcher), a high-performance cross-matching framework based on spherical position deviation on CPU-GPU heterogeneous computing platforms. It supports scalable and flexible cross-matching and can be directly applied to the fusion of large astronomical catalogues from survey missions and astronomical data centers. A performance estimation model is proposed to locate the performance bottlenecks and guide the optimizations. A two-level partitioning strategy is designed to generate an optimized data placement according to the positions of celestial objects to increase throughput. To make HLC2 a more adaptive solution, the architecture-aware task splitting, thread parallelization and concurrent scheduling strategies are designed and integrated. Moreover, a novel quad-direction strategy is proposed for the boundary problem to effectively balance performance and completeness. We have experimentally evaluated HLC2 using public released catalogue data. Experiments demonstrate that HLC2 scales well on different sizes of catalogues and the cross-matching speed is significantly improved compared to the state-of-the-art cross-matchers.

Telescope arrays are receiving increasing attention due to their promise of higher resource utilization, greater sky survey area, and higher frequency of full space-time monitoring than single telescopes. Compared with the ordinary coordinated operation of several telescopes, the new astronomical observation mode has an order of magnitude difference in the number of telescopes. It requires efficient coordinated observation by large-domain telescopes distributed at different sites. Coherent modeling of various abstract environmental constraints is essential for responding to multiple complex science goals. Also, due to competing science priorities and field visibility, how the telescope arrays are scheduled for observations can significantly affect observation efficiency. This paper proposes a multilevel scheduling model oriented toward the problem of telescope-array scheduling for time-domain surveys. A flexible framework is developed with basic functionality encapsulated in software components implemented on hierarchical architectures. An optimization metric is proposed to self-consistently weight contributions from time-varying observation conditions to maintain uniform coverage and efficient time utilization from a global perspective. The performance of the scheduler is evaluated through simulated instances. The experimental results show that our scheduling framework performs correctly and provides acceptable solutions considering the percentage of time allocation efficiency and sky coverage uniformity in a feasible amount of time. Using a generic version of the telescope-array scheduling framework, we also demonstrate its scalability and its potential to be applied to other astronomical applications.

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.