Optimizing VGOS observations using an SNR-based scheduling approach
Bibliographic record
Abstract
Abstract The geodetic and astrometric very long baseline interferometry (VLBI) community is in the process of upgrading its existing infrastructure with the VLBI Global Observing System (VGOS). The primary objective of VGOS is to substantially boost the number of scans per hour for enhanced parameter estimation. However, the current observing strategy results in fewer scans than anticipated. During 2022, six 24-h VGOS Research and Development (R&D) sessions were conducted to demonstrate a proof-of-concept aimed at addressing this shortcoming. The new observation strategy centers around a signal-to-noise (SNR)-based scheduling approach combined with eliminating existing overhead times in existing VGOS sessions. Two SNR-based scheduling approaches were tested during these sessions: one utilizing inter-/extrapolation of existing S/X source flux density models and another based on a newly derived source flux density catalog at VGOS frequencies. Both approaches proved effective, leading to a 2.3-fold increase in the number of scheduled scans per station and a 2.6-fold increase in the number of observations per station while maintaining a high observation success rate of approximately 90 % to 95 %. Consequently, both strategies succeeded in the main objective of these sessions by successfully increasing the number of scans per hour. The strategies described in this work can be easily applied to operational VGOS observations. Besides outlining and discussing the observation strategy, we further provide insight into the resulting signal-to-noise ratios, and discuss the impact on the precision of the estimated geodetic parameters. Monte Carlo simulations predicted a roughly 50 % increase in geodetic precision compared to operational VGOS sessions. The analysis confirmed that the formal errors in estimated station coordinates were reduced by 40 % to 50 %. In addition, Earth orientation parameters showed significant improvement, with a 40 % to 50 % reduction in formal errors. Graphical Abstract
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".