Efficient Parallel Processing of Second-Generation TDI Data for Galactic Binaries in Space-Based Gravitational Wave Missions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".