Search for continuous gravitational waves directed at subthreshold radiometer candidates in O3 LIGO data
Bibliographic record
Abstract
We present results of a follow-up search for continuous gravitational waves (CWs) associated with subthreshold candidates from the LIGO-Virgo-KAGRA (LVK) all-sky all-frequency (ASAF) directed radiometer analysis, using Advanced LIGO data from the third observing run (O3). Each ASAF candidate corresponds to a $1/32\text{ }\text{ }\mathrm{Hz}$ frequency band and $\ensuremath{\sim}13\text{ }\text{ }{\mathrm{deg}}^{2}$ sky pixel. Assuming they represent possible CW sources, we analyze all 515 ASAF candidates using a semi-coherent, $\mathcal{F}$-statistic-based matched filter search. The search algorithm incorporates a hidden Markov model (HMM), expanding the signal model to allow frequency spin-wandering, as well as unmodeled frequency evolution of less than ${10}^{\ensuremath{-}5}\text{ }\text{ }\mathrm{Hz}$ per day that is not captured by the searched range of $\ifmmode\pm\else\textpm\fi{}{10}^{\ensuremath{-}9}\text{ }\text{ }\mathrm{Hz}/\mathrm{s}$ in frequency derivative. Significance thresholds with a 5% probability of false alarm per ASAF candidate are determined empirically by searching detector noise at various off-target sky positions. We obtain 14 outliers surviving a set of vetoes designed to eliminate instrumental artifacts. Upon further investigation, these outliers are deemed unlikely to represent astrophysical signals. We estimate the sensitivity of our search to both isolated and binary sources with orbital period greater than one year by recovering simulated signals added to detector data. The minimum detectable strain amplitude at 95% confidence for isolated (long-period binary) sources is ${h}_{0}^{95%}=8.8\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}26}$ ($9.4\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}26}$) at a frequency of 222.6 Hz. While this study focuses on ASAF subthreshold candidates, the method presented could be applied to follow up candidates from future all-sky CW searches, complementing currently existing methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".