An Observational Upper Limit on the Interstellar Number Density of Asteroids and Comets
Bibliographic record
Abstract
Abstract We derived 90% confidence limits (CLs) on the interstellar number density ( ) of interstellar objects (ISOs; comets and asteroids) as a function of the slope of their size–frequency distribution (SFD) and limiting absolute magnitude. To account for gravitational focusing, we first generated a quasi-realistic ISO population to from the Sun and propagated it forward in time to generate a steady state population of ISOs with heliocentric distance . We then simulated the detection of the synthetic ISOs using pointing data for each image and average detection efficiencies for each of three contemporary solar system surveys—Pan-STARRS1, the Mt. Lemmon Survey, and the Catalina Sky Survey. These simulations allowed us to determine the surveys’ combined ISO detection efficiency under several different but realistic modes of identifying ISOs in the survey data. Some of the synthetic detected ISOs had eccentricities as small as 1.01, which is in the range of the largest eccentricities of several known comets. Our best CL of implies that the expectation that extra-solar systems form like our solar system, eject planetesimals in the same way, and then distribute them throughout the Galaxy, is too simplistic, or that the SFD or behavior of ISOs as they pass through our solar system is far from expectation.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".