An improved pipeline to search for gravitational waves from compact binary coalescence
Bibliographic record
Abstract
The second generation of ground-based gravitational-wave detectors will begin taking data in September 2015. Sensitive and computationally-efficient data analysis methods will be required to maximize what we learn from their observations. We describe improvements made to the offline analysis pipeline searching for gravitational waves from stellar-mass compact binary coalescences, and assess how these improvements affect search sensitivity. Starting with the two-stage ihope pipeline used in S5, S6 and VSR1-3 and using two weeks of S6/VSR3 data as test periods, we first demonstrate a pipeline with a simpler workflow. This single-stage pipeline performs matched filtering and coincidence testing only once. This simplification allows us to reach much lower false-alarm rates for loud candidate events. We then describe an optimized chi-squared test which minimizes computational cost. Next, we compare methods of generating template banks, demonstrating that a fixed bank may be used for extended stretches of time. Fixing the bank reduces the cost and complexity, compared to the previous method of regenerating a template bank every 2048 s of analyzed data. Creating a fixed bank shared by all detectors also allows us to apply a more stringent coincidence test, whose performance we quantify. With these improvements, we find a 10% increase in sensitive volume with a negligible change in computational cost.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".