Architecture of Tianyu Software: Relative Photometry as a Case Study

TL;DR

This work tackles the challenge of processing Tianyu's high-rate optical data (>500 MB/s) through a scalable, automated software stack. It introduces a publisher–consumer pipeline managed by a MySQL database and RabbitMQ, complemented by a relational database and a file-system separation to automate calibration, stacking, cross-matching, and flux extraction. A relative photometry pipeline demonstrates strong scalability and precision on real data from Muguang and Xinglong, detecting two transiting sources and identifying multiple variable stars with CDPP near the theoretical limit. These results establish Tianyu as a scalable foundation for automated exoplanet discovery and time-domain astronomy, with code available on GitHub for broader adoption.

Abstract

Tianyu telescope, an one-meter robotic optical survey instrument to be constructed in Lenghu, Qinghai, China, is designed for detecting transiting exoplanets, variable stars and transients. It requires a highly automated, optimally distributed, easily extendable, and highly flexible software to enable the data processing for the raw data at rates exceeding 500MB/s. In this work, we introduce the architecture of the Tianyu pipeline and use relative photometry as a case to demonstrate its high scalability and efficiency. This pipeline is tested on the data collected from Muguang observatory and Xinglong observatory. The pipeline demonstrates high scalability, with most processing stages increasing in throughput as the number of consumers grows. Compared to a single consumer, the median throughput of image calibration, alignment, and flux extraction increases by 41%, 257%, and 107% respectively when using 5 consumers, while image stacking exhibits limited scalability due to I/O constraints. In our tests, the pipeline was able to detect two transiting sources. Besides, the pipeline captures variability in the light curves of nine known and two previously unknown variable sources in the testing data. Meanwhile, the differential photometric precision of the light curves is near the theoretical limitation. These results indicate that this pipeline is suitable for detecting transiting exoplanets and variable stars. This work builds the fundation for further development of Tianyu software. Code of this work is available at https://github.com/ruiyicheng/Tianyu_pipeline.

Figures (10)

• Figure 1: Architecture of Tianyu software. See details in Section \ref{['sec:architecture']}.
• Figure 2: A general coupling relationship between the pipeline and the database. See details in Section \ref{['sec:database']}.
• Figure 3: Data flow of the Tianyu pipeline. Blocks with black labels represent stored data entities generated by this pipeline, as illustrated in Fig. \ref{['fig:couplingdbpipe']}. Arrows indicate the direction of data flow and associated processing operations. Blocks with gray labels and dashed arrows denote components planned for implementation in future work. Further details are provided in Section \ref{['sec:pipelines']}.
• Figure 4: Demonstration of hierarchical stacking while stacking 13 images with fan-out equals to 3. A rectangle represents an image; number on the rectangles represents the total number of high-quality progenetor image; arrows point from the progenetor image to the stacked image. See Section \ref{['sec:stacking']} for details.
• Figure 5: Relationship between the throughput speed-up relative to the single consumer and the number of deployed consumers. Consumers were deployed manually. Throughput values are obtained by the inverse execution time. The absolute total throughput as measured in s$^{-1}$ is available in Table \ref{['tab:multi_node']}.