Innovations in Modern Survey Simulation: Predicting Interstellar Objects in LSST
Bibliographic record
Abstract
Survey simulation is a numerical tool used to contextualize the discoveries of modern surveys. In Solar System science, telescopic campaigns yield biased subsets of the overall intrinsic small body populations. Understanding the observational biases of a given group of objects is imperative to interpreting the survey discoveries (or lack thereof).Interstellar objects (ISOs) are planetesimals, either asteroidal or cometary, which are unbound from their origin planetary system. ISOs are expected to be bountiful in the Milky Way; simulations have shown that the Solar System ejected most of its planetesimals during its early evolution and it is sensible to extrapolate that other planetary systems may do the same. Even so, to date there have only been two confirmed serendipitous ISO passages through the Solar System, 1I\`Oumuamua and 2I\Borisov. Discovery of the next interstellar object, 3I, is uncertain; the physical size and number density of ISOs are largely unconstrained. However, recent analysis of the solar neighbourhood using data from the Gaia mission (Hopkins et al. 2025) has provided a model for the characteristics of the local ISO population with which to survey simulate discoveries in upcoming surveys.In this dissertation talk, I will discuss the challenges of simulating ISOs in a realistic survey, introduce the new innovations in survey simulating used in this work (in comparison to historical approaches), and provide probabilistic predictions for the characteristics of the ISO discoveries in the upcoming Legacy Survey of Space and Time (LSST).
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".