The NANOGrav 11 yr Data Set: Solar Wind Sounding through Pulsar Timing
Bibliographic record
Abstract
The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) has observed dozens of millisecond pulsars for over a decade. We have accrued a large collection of dispersion measure (DM) measurements sensitive to the total electron content between Earth and the pulsars at each observation. All lines of sight cross through the solar wind (SW), which produces correlated DM fluctuations in all pulsars. We develop and apply techniques for extracting the imprint of the SW from the full collection of DM measurements in the recently released NANOGrav 11 yr data set. We filter out long-timescale DM fluctuations attributable to structure in the interstellar medium and carry out a simultaneous analysis of all pulsars in our sample that can differentiate the correlated signature of the wind from signals unique to individual lines of sight. When treating the SW as spherically symmetric and constant in time, we find the electron number density at 1 au to be 7.9 +/- 0.2 cm(-3). We find our data to be insensitive to long-term variation in the density of the wind. We argue that our techniques paired with a high-cadence, low-radio-frequency observing campaign of near-ecliptic pulsars would be capable of mapping out large-scale latitudinal structure in the wind.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".