<i>Swift</i>-XRT follow-up of gravitational wave triggers during the third aLIGO/Virgo observing run
Bibliographic record
Abstract
ABSTRACT The Neil Gehrels Swift Observatory followed up 18 gravitational wave (GW) triggers from the LIGO/Virgo collaboration during the O3 observing run in 2019/2020, performing approximately 6500 pointings in total. Of these events, four were finally classified (if real) as binary black hole (BH) triggers, six as binary neutron star (NS) events, two each of NSBH and Mass Gap triggers, one an unmodelled (Burst) trigger, and the remaining three were subsequently retracted. Thus far, four of these O3 triggers have been formally confirmed as real gravitational wave events. While no likely electromagnetic counterparts to any of these GW events have been identified in the X-ray data (to an average upper limit of 3.60 × 10−12 erg cm−2 s−1 over 0.3–10 keV), or at other wavelengths, we present a summary of all the Swift-XRT observations performed during O3, together with typical upper limits for each trigger observed. The majority of X-ray sources detected during O3 were previously uncatalogued; while some of these will be new (transient) sources, others are simply too faint to have been detected by earlier survey missions such as ROSAT. The all-sky survey currently being performed by eROSITA will be a very useful comparison for future observing runs, reducing the number of apparent candidate X-ray counterparts by up to 95 per cent.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".