Modelling the light curve of ‘Oumuamua: evidence for torque and disc-like shape
Bibliographic record
Abstract
ABSTRACT We present the first attempt to fit the light curve of the interstellar visitor ‘Oumuamua using a physical model that includes optional torque. We consider both conventional (Lommel–Seeliger triaxial ellipsoid) and alternative (‘black-and-white ball’, ‘solar sail’) brightness models. With all the brightness models, some torque is required to explain the timings of the most conspicuous features – deep minima – of the asteroid’s light curve. Our best-fitting models are a thin disc (aspect ratio 1:6) and a thin cigar (aspect ratio 1:8) that are very close to being axially symmetric. Both models are tumbling and require some torque that has the same amplitude in relation to ‘Oumuamua’s linear non-gravitational acceleration as in Solar system comets whose dynamics is affected by outgassing. Assuming random orientation of the angular momentum vector, we compute probabilities for our best-fitting models. We show that cigar-shaped models suffer from a fine-tuning problem and have only 16 per cent probability to produce light-curve minima as deep as the ones present in ‘Oumuamua’s light curve. Disc-shaped models, on the other hand, are very likely (at 91 per cent) to produce minima of the required depth. From our analysis, the most likely model for ‘Oumuamua is a thin disc (slab) experiencing moderate torque from outgassing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".