NANOGrav CONSTRAINTS ON GRAVITATIONAL WAVE BURSTS WITH MEMORY
Bibliographic record
Abstract
Among efforts to detect gravitational radiation, pulsar timing arrays are uniquely poised to detect "memory" signatures, permanent perturbations in spacetime from highly energetic astrophysical events such as mergers of supermassive black hole binaries. The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) observes dozens of the most stable millisecond pulsars using the Arecibo and Green Bank radio telescopes in an effort to study, among other things, gravitational wave memory. We herein present the results of a search for gravitational wave bursts with memory (BWMs) using the first five years of NANOGrav observations. We develop original methods for dramatically speeding up searches for BWM signals. In the directions of the sky where our sensitivity to BWMs is best, we would detect mergers of binaries with reduced masses of out to distances of 30 Mpc; such massive mergers in the Virgo cluster would be marginally detectable. We find no evidence for BWMs. However, with our non-detection, we set upper limits on the rate at which BWMs of various amplitudes could have occurred during the time spanned by our data—e.g., BWMs with amplitudes greater than 10 −13 must encounter the Earth at a rate less than 1.5 yr −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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".