One-stop strategy to search for long-duration gravitational-wave signals
Bibliographic record
Abstract
Blind continuous gravitational-wave (CWs) searches are a significant computational challenge due to their long duration and weak amplitude of the involved signals. To cope with such problem, the community has developed a variety of data-analysis strategies which are usually tailored to specific CW searches; this prevents their applicability across the nowadays broad landscape of potential CW source. Also, their sensitivity is typically hard to model, and thus usually requires a significant computing investment. We present fasttracks, a massively-parallel engine to evaluate detection statistics for generic CW signals using GPU computing. We demonstrate a significant increase in computational efficiency by parallelizing the brute-force evaluation of detection statistics without using any computational approximations. Also, we introduce a simple and scalable postprocessing which allows us to formulate a generic semianalytic sensitivity estimate algorithm. These proposals are tested in a minimal all-sky search in data from the third observing run of the LIGO-Virgo-KAGRA Collaboration. The strategies discussed here will become increasingly relevant in the coming years as long-duration signals become a standard observation of future ground-based and space-borne detectors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".