All-sky search for short gravitational-wave bursts in the first part of the fourth LIGO-Virgo-KAGRA observing run
Bibliographic record
Abstract
We present a search for short-duration gravitational-wave transients in data from the first eight months of Advanced LIGO-Virgo-KAGRA's fourth observing run, denoted O4a. We use four analyses which are sensitive to a wide range of potential signals lasting up to a few seconds in the 16-4096 Hz band. Excluding binary black hole merger candidates that were already identified by low-latency analyses, we find no statistically significant evidence for other gravitational-wave transients. We measure the sensitivity of the search for representative signals, including sine-Gaussians, Gaussian pulses, and white-noise bursts with different frequencies and durations, adopting a false alarm rate of 1 per 100 years as detection threshold. Depending on signal type, we find improvements over previous searches by factors of 2 to 10 in terms of sensitivity to strain amplitude and of 90% confidence upper limit on the rate density of sources. We also evaluate a variety of core-collapse supernova models and find that, for some models, the search could have detected gravitational waves from stellar core-collapse throughout the Milky Way. Finally, we consider neutron star f-modes associated with pulsar glitches and find that, assuming a source similar to the Vela Pulsar, the search could have detected a gravitational-wave signal from a glitch with fractional frequency change as small as $\sim 2$ to $6 \times 10^{-5}$ depending on the neutron star mass.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".