THE NANOGRAV NINE-YEAR DATA SET: MONITORING INTERSTELLAR SCATTERING DELAYS
Bibliographic record
Abstract
ABSTRACT We report on an effort to extract and monitor interstellar scintillation parameters in regular timing observations collected for the North American Nanohertz Observatory for Gravitational Waves pulsar timing array. Scattering delays are measured by creating dynamic spectra for each pulsar and observing epoch of wide-band observations centered near 1500 MHz and carried out at the Green Bank Telescope and the Arecibo Observatory. The ∼800 MHz wide frequency bands imply dramatic changes in scintillation bandwidth across the bandpass, and a stretching routine has been included to account for this scaling. For most of the 10 pulsars for which the scaling has been measured, the bandwidths scale with frequency less steeply than expected for a Kolmogorov medium. We find estimated scattering delay values that vary with time by up to an order of magnitude. The mean measured scattering delays are similar to previously published values and are slightly higher than predicted by interstellar medium models. We investigate the possibility of increasing the timing precision by mitigating timing errors introduced by the scattering delays. For most of the pulsars, the uncertainty in the time of arrival of a single timing point is much larger than the maximum variation of the scattering delay, suggesting that diffractive scintillation remains as only a negligible part of their noise budget.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".