Subtracting glitches from gravitational-wave detector data during the third LIGO-Virgo observing run
Bibliographic record
Abstract
Abstract Data from ground-based gravitational-wave detectors contains numerous short-duration instrumental artifacts, called ‘glitches’. The high rate of these artifacts in turn results in a significant fraction of gravitational-wave signals from compact binary coalescences overlapping glitches. In LIGO-Virgo’s third observing run, ≈20% of gravitational-wave source candidates required some form of mitigation due to glitches. This was the first observing run where glitch subtraction was included as a part of LIGO-Virgo-KAGRA data analysis methods for a large fraction of detected gravitational-wave events. This work describes the methods to identify glitches, the decision process for deciding if mitigation was necessary, and the two algorithms, BayesWave and gwsubtract, that were used to model and subtract glitches. Through case studies of two events, GW190424_180648 and GW200129_065458, we evaluate the effectiveness of the glitch subtraction, compare the statistical uncertainties in the relevant glitch models, and identify potential limitations in these glitch subtraction methods. We finally outline the lessons learned from this first-of-its-kind effort for future observing runs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".