A Catalogue of Interstellar Material Delivery from Nearby Debris Disks
Bibliographic record
Abstract
Abstract We modeled the trajectories of material ejected from 20 nearby debris disk stars, including ϵ Eridani (Ran), Vega, Fomalhaut, and β Pictoris, within a simulated Milky Way potential in order to quantify their contribution to the population of interstellar material entering the solar system. Our simulations show that material from each of these 20 systems is currently to be expected within our planetary system. We calculate expected fluxes of both macroscopic interstellar objects (ISOs, ≥100 m), which could be detected by telescopic surveys, and smaller meteoroids (≥200 μ m), which could manifest as meteors in Earth’s atmosphere. We estimate that the ISO population originating from these debris disks and currently within the inner solar system is on the order of ∼2, only a fraction of the expected total ISO population but nonetheless likely to be discovered by Rubin. Meteors in Earth’s atmosphere from these systems are expected as well, but current methods, both radar and video, might require decades to collect even a single event. Our sample is found to be rich in relatively low excess velocity particles compared to the broader expected ISO population, which might make them harder to distinguish observationally from bound objects in some cases. These results provide a framework for linking detections of interstellar material to their astrophysical origins, offering new opportunities to probe the composition and dynamical history of nearby planetary systems.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".