Upper limit map of a background of gravitational waves
Bibliographic record
Abstract
We searched for an anisotropic background of gravitational waves usingdata from the LIGO S4 science run and a method that is optimizedfor point sources. This is appropriate if, for example, the gravitationalwave background is dominated by a small number of distinct astrophysical sources.No signal was seen. Upper limit maps were produced assuming two differentpower laws for the source strain power spectrum. For an ${f}^{\ensuremath{-}3}$ power law and using the50 Hz to 1.8 kHz band the upper limits on the sourcestrain power spectrum vary between $1.2\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}48}\text{ }\text{ }{\mathrm{Hz}}^{\ensuremath{-}1}$ $(100\text{ }\text{ }\mathrm{Hz}/f{)}^{3}$ and $1.2\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}47}\text{ }\text{ }{\mathrm{Hz}}^{\ensuremath{-}1}$ $(100\text{ }\text{ }\mathrm{Hz}/f{)}^{3}$, depending on the position in the sky. Similarly,in the case of constant strain power spectrum, the upper limits vary between $8.5\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}49}\text{ }\text{ }{\mathrm{Hz}}^{\ensuremath{-}1}$ and $6.1\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}48}\text{ }\text{ }{\mathrm{Hz}}^{\ensuremath{-}1}$. As a side product a limiton an isotropic background of gravitational waves was also obtained. All limitsare at the 90% confidence level. Finally, as an application, we focused onthe direction of Sco-X1, the brightest low-mass x-ray binary. We compare theupper limit on strain amplitude obtained by this method to expectations basedon the x-ray flux from Sco-X1.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".