Research on asymmetric spatial heterodyne spectroscopy for velocimetry navigation
Bibliographic record
Abstract
Abstract The further development of deep space exploration contributes to the exploration of the universe and the origin and evolution of life on Earth, and is a prerequisite and foundation for the development and utilization of space resources. Autonomous navigation of deep space probes, one of the key technologies for deep space exploration, can significantly reduce ground support costs and improve the autonomous operation, management, and on-orbit survivability of deep space probes. Among them, autonomous astronomical navigation methods based on velocity measurements directly measure velocity information, effectively avoiding the impact of using calculus to solve velocity on response time and navigation accuracy in navigation methods based on angle and distance measurements. The passive radial velocity measurement technique of asymmetric spatial heterodyne spectroscopy has the advantages of compact structure, large luminous flux, and multiple spectra detected simultaneously. Taking the Sun as the navigation target source, we carried out the selection of observational spectral lines and the parameter design of the velocimetry navigation system, designed the calculation of phases for the absorption characteristic line under the complex polychromatic strong background, carried out the simulation analysis of velocimetry under the typical relative velocimetry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".