Efficient Parallel Processing of Second-Generation TDI Data for Galactic Binaries in Space-Based Gravitational Wave Missions

Space-based gravitational wave missions such as LISA, Taiji, and Tianqin rely on the time-delay interferometry (TDI) technique to observe low-frequency signals such as Galactic binaries (GBs), massive black-hole binaries, and extreme-mass-ratio inspirals. Among these sources, resolving the large population of GBs poses a central challenge for data analysis. In this work, we present GBSIEVER-C, a pipeline implemented in C and parallelized using OpenMP (Open Multi-Processing), along with a range of additional algorithmic optimizations, including a fast implementation of second-generation TDI response modeling. It builds upon the previous MATLAB-based pipeline that demonstrated competitive performance on LISA Data Challenge (LDC) data. To the best of our knowledge, GBSIEVER-C is the first pipeline to address the GB resolution problem using second-generation TDI data. We apply it to the GB dataset in Taiji Data Challenge (TDC) that contains 30 million GBs. Compared with our previous results on LDC data, it achieves improved source resolution, residual suppression, and parameter-estimation accuracy. These gains are consistent with the enhanced sensitivity expected from Taiji’s longer arm length. Although validated on Taiji data, the pipeline is fully compatible with LISA and similar mission configurations, and supports both single-detector and multi-detector network analyses.