A Study of GPS Carrier-Phase Time Transfer Noise Based on NIST GPS Receivers
Bibliographic record
Abstract
To do a better time comparison between high-precision clocks (such as a Cesium-fountain clock and Hydrogen-maser clock), we want to study and eventually lower the GPS carrier-phase time transfer noise.The GPS carrier-phase time transfer noise comes from four sources: GPS satellite, GPS signal path, ground receiving equipment (receiver and antenna), and data-processing algorithm.This paper focuses on the noise introduced by the ground receiving equipment.At NIST, we have installed seven GPS receivers.All receivers have the same reference time, i.e., UTC(NIST).Three of them are connected to the same antenna.The other four are connected to four different antennas.This architecture enables us to study the time-transfer noise from the ground receiving equipment.We study both long-term (> 100 days) noise and short-term (< 1 day) noise.For the long-term noise, the time-transfer result using one receiver can vary from that using another receiver by up to 1.8 ns, during 1.3 years.To achieve sub-nanosecond GPS timing accuracy, a careful monitoring of the time delays or a more frequent calibration is needed.For the short-term noise, we find that the common-clock difference between receivers using the same antenna is less noisy than that using two different antennas, at an averaging time of less than 0.5 hour.This indicates that the antenna and antenna cable contribute to the super-short-term noise of GPS carrier-phase time transfer significantly.In addition, the response to the GPS receiver's reference-time change is tested in this paper.The variation in the response can be up to ± 350 ps.Last, this paper gives the best carrier-phase time transfer result we can currently achieve with the available equipment at NIST.The best frequency stability is 4.0×10 -16 at 3 hours, 1.1×10 -16 at 1 day, 4.0×10 -17 at 10 days, and 1.3×10 -17 at 48 days.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".