The NANOGrav 12.5 yr Data Set: The Frequency Dependence of Pulse Jitter in Precision Millisecond Pulsars
Bibliographic record
Abstract
Abstract Low-frequency gravitational-wave experiments require the highest timing precision from an array of the most stable millisecond pulsars. Several known sources of noise on short timescales in single radio pulsar observations are well described by a simple model of three components: template fitting from a finite signal-to-noise ratio, pulse phase/amplitude jitter from single-pulse stochasticity, and scintillation errors from short-timescale interstellar scattering variations. Currently template-fitting errors dominate, but as radio telescopes push toward higher signal-to-noise ratios, jitter becomes the next dominant term for most millisecond pulsars. Understanding the statistics of jitter becomes crucial for properly characterizing arrival time uncertainties. We characterize the radio frequency dependence of jitter using data on 48 pulsars in the North American Nanohertz Observatory for Gravitational Waves timing program. We detect significant jitter in 43 of the pulsars and test several functional forms for its frequency dependence; we find significant frequency dependence for 30 pulsars. We find moderate correlations of rms jitter with pulse width (R = 0.62) and number of profile components (R = 0.40); the single-pulse rms jitter is typically ≈1% of pulse phase. The average frequency dependence for all pulsars using a power-law model has index −0.42. We investigate the jitter variations for the interpulse of PSR B1937+21 and find no significant deviations from the main pulse rms jitter. We also test the time variation of jitter in two pulsars and find that systematics likely bias the results for high-precision pulsars. Pulsar timing array analyses must properly model jitter as a significant component of the noise within the detector.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".