The completeness-corrected rate of stellar encounters with the Sun from the first
Bibliographic record
Abstract
I report on close encounters of stars to the Sun found in the first Gaia data release (GDR1). Combining Gaia astrometry with radial velocities of around 320 000 stars drawn from various catalogues, I integrate orbits in a Galactic potential to identify those stars which pass within a few parsecs. Such encounters could influence the solar system, for example through gravitational perturbations of the Oort cloud. 16 stars are found to come within 2 pc (although a few of these have dubious data). This is fewer than were found in a similar study based on Hipparcos data, even though the present study has many more candidates. This is partly because I reject stars with large radial velocity uncertainties (>10 km s-1), and partly because of missing stars in GDR1 (especially at the bright end). The closest encounter found is Gl 710, a K dwarf long-known to come close to the Sun in about 1.3 Myr. The Gaia astrometry predict a much closer passage than pre-Gaia estimates, however: just 16 000 AU (90% confidence interval: 10 000–21 000 AU), which will bring this star well within the Oort cloud. Using a simple model for the spatial, velocity, and luminosity distributions of stars, together with an approximation of the observational selection function, I model the incompleteness of this Gaia-based search as a function of the time and distance of closest approach. Applying this to a subset of the observed encounters (excluding duplicates and stars with implausibly large velocities), I estimate the rate of stellar encounters within 5 pc averaged over the past and future 5 Myr to be 545 ± 59 Myr-1. Assuming a quadratic scaling of the rate within some encounter distance (which my model predicts), this corresponds to 87 ± 9 Myr-1 within 2 pc. A more accurate analysis and assessment will be possible with future Gaia data releases.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".