On-Orbit Observations of Conjuncting Space Objects Prior to the Time of Closest Approach
Bibliographic record
Abstract
Abstract Conjunction assessment of space objects in Low Earth Orbit (LEO) generally uses information collected by ground-based space surveillance sensors. These sensors track both the primary object (normally an active satellite) and the secondary object (typically space debris). The tracking data is used to update both objects’ orbits for collision risk assessment. The primary satellite’s involvement in this process is that of a satellite in jeopardy - the primary satellite does not usually contribute tracking data on the secondary as they are typically unequipped to do so. In this paper, an examination how an at-risk LEO primary satellite could obtain optical tracking data on a secondary object prior to the Time of Closest Approach (TCA) and assess its own collision risk without the need for additional ground-based space surveillance data is performed. This analysis was made possible by using in-situ optical measurements of space objects conjuncting with the Canadian NEOSSat Space Situational Awareness R&D microsatellite. By taking advantage of the near “constant-bearing, decreasing range” observing geometry formed during a LEO conjunction, NEOSSat can collect astrometric and photometric measurements of the secondary object in the time prior to TCA, or in the multiple half-orbits preceding TCA. This paper begins by describing the in-situ phenomenology of optically observed conjunctions in terms of the observing approach, geometry and detected astrometric and photometric characteristics. It was found that conjuncting objects are detectable to magnitude 16 and astrometric observations can be used for position covariances in the computation of probability of collision. Illustrative examples are provided. In orbits prior to TCA, in-track positioning error is improved by a factor of two or more by processing space-based observations on a filtered position estimate of the secondary. However, cross-track positioning knowledge is negligibly improved due to the inherent astrometric measurement precision of the NEOSSat sensor and the oblique observing geometry during conjunction observations. A short analysis of object detectability where star trackers could be used to perform similar observations finds that larger payload-sized objects would generally be detectable. However, smaller debris objects would require higher sensitivity from the star tracker if employed for optical conjunction derisk observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".