The Galactic Interstellar Object Population: A Framework for Prediction and Inference
Bibliographic record
Abstract
Abstract The Milky Way is thought to host a huge population of interstellar objects (ISOs), numbering approximately 1015 pc−3 around the Sun, which are formed and shaped by a diverse set of processes ranging from planet formation to Galactic dynamics. We define a novel framework, first to predict the properties of this Galactic ISO population by combining models of processes across planetary and Galactic scales, and second to make inferences about the processes being modeled, by comparing the predicted population to what is observed. We predict the spatial and compositional distribution of the Galaxy’s population of ISOs by modeling the Galactic stellar population with data from the APOGEE survey and combining this with a protoplanetary disk chemistry model. Selecting the ISO water mass fraction as an example observable quantity, we evaluate its distribution both at the position of the Sun and averaged over the Galactic disk; our prediction for the solar neighborhood is compatible with the inferred water mass fraction of 2I/Borisov. We show that the well-studied Galactic stellar metallicity gradient has a corresponding ISO compositional gradient. We also demonstrate the inference part of the framework by using the current observed ISO composition distribution to constrain the parent star metallicity dependence of the ISO production rate. This constraint, and other inferences made with this framework, will improve dramatically as the Vera C. Rubin Observatory Legacy Survey of Space and Time progresses and more ISOs are observed. Finally, we explore generalizations of this framework to other Galactic populations, such as that of exoplanets.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".