Probing more deeply in an all-sky search for continuous gravitational waves in the LIGO O3 data set
Bibliographic record
Abstract
We report results from an all-sky search of the LIGO data from the third LIGO-Virgo-KAGRA run (O3) for continuous gravitational waves from isolated neutron stars in the frequency band [30, 150] Hz and spindown range of $[\ensuremath{-}1\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}8},+1\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}9}]\text{ }\text{ }\mathrm{Hz}/\mathrm{s}$. This search builds upon a previous analysis of the first half of the O3 data using the same powerflux pipeline. We search more deeply here by using the full O3 data and by using loose coherence in the initial stage with fully coherent combination of LIGO Hanford (H1) and LIGO Livingston (L1) data, while limiting the frequency band searched and excluding narrow, highly disturbed spectral bands. We detect no signal and set strict frequentist upper limits on circularly polarized and on linearly polarized wave amplitudes, in addition to estimating population-averaged upper limits. The lowest upper limit obtained for circular polarization is $\ensuremath{\sim}4.5\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}26}$, and the lowest linear polarization limit is $\ensuremath{\sim}1.3\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}25}$ (both near 144 Hz). The lowest estimated population-averaged upper limit is $\ensuremath{\sim}1.0\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}25}$. In the frequency band and spindown range searched here, these limits improve upon the O3a powerflux search by a median factor of $\ensuremath{\sim}1.4$ and upon the best previous limits obtained for the full O3 data by a median factor of $\ensuremath{\sim}1.1$.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".