Search for gravitational waves from binary inspirals in S3 and S4 LIGO data
Bibliographic record
Abstract
We report on a search for gravitational waves from the coalescence of compact binaries during the third and fourth LIGO science runs. The search focused on gravitational waves generated during the inspiral phase of the binary evolution. In our analysis, we considered three categories of compact binary systems, ordered by mass: (i) primordial black hole binaries with masses in the range $0.35{M}_{\ensuremath{\bigodot}}<{m}_{1}$, ${m}_{2}<1.0{M}_{\ensuremath{\bigodot}}$, (ii) binary neutron stars with masses in the range $1.0{M}_{\ensuremath{\bigodot}}<{m}_{1}$, ${m}_{2}<3.0{M}_{\ensuremath{\bigodot}}$, and (iii) binary black holes with masses in the range $3.0{M}_{\ensuremath{\bigodot}}<{m}_{1}$, ${m}_{2}<{m}_{\mathrm{max}}$ with the additional constraint ${m}_{1}+{m}_{2}<{m}_{\mathrm{max}}$, where ${m}_{\mathrm{max}}$ was set to $40.0{M}_{\ensuremath{\bigodot}}$ and $80.0{M}_{\ensuremath{\bigodot}}$ in the third and fourth science runs, respectively. Although the detectors could probe to distances as far as tens of Mpc, no gravitational-wave signals were identified in the 1364 hours of data we analyzed. Assuming a binary population with a Gaussian distribution around $0.75\ensuremath{-}0.75{M}_{\ensuremath{\bigodot}}$, $1.4\ensuremath{-}1.4{M}_{\ensuremath{\bigodot}}$, and $5.0\ensuremath{-}5.0{M}_{\ensuremath{\bigodot}}$, we derived 90%-confidence upper limit rates of $4.9\text{ }\text{ }{\mathrm{yr}}^{\ensuremath{-}1}{L}_{10}^{\ensuremath{-}1}$ for primordial black hole binaries, $1.2\text{ }\text{ }{\mathrm{yr}}^{\ensuremath{-}1}{L}_{10}^{\ensuremath{-}1}$ for binary neutron stars, and $0.5\text{ }\text{ }{\mathrm{yr}}^{\ensuremath{-}1}{L}_{10}^{\ensuremath{-}1}$ for stellar mass binary black holes, where ${L}_{10}$ is ${10}^{10}$ times the blue-light luminosity of the Sun.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".