Tracing Stellar Close Encounters with Our Sun from GAIA DR2, LAMOST DR4, and RAVE DR5 Catalogues
Bibliographic record
Abstract
Abstract Our Sun is surrounded by the Oort Cloud (in radius 0.5 pc) which can be perturbed by various external factors. One of those is the stellar close encounter with our Sun. This kind of perturbation can induce the cometary showers in our Solar System. In this work, we attempt to make numerical simulations to trace the orbit of stars which close encounter with our Sun in the cases of Milky Way’s axisymmetric only and with non-axisymmetric potentials. We have 306 selected solar neighborhood stars from GAIA DR2, LAMOST DR4, and RAVE DR5 which have highly precise kinematics. In this work, we find a few stars that have counter parameter (dm ) less than or equal to 2 pc in both of past and future close encounters with the Sun. We also find a few stars (ID 283, 290, 297, 298) even with dm ≤ 0.5 pc within their errors, for past close encounters at time tm ≥ 0.5 Myr ago. These stars should have perturbed the Oort Cloud’s stability long time ago. Furthermore, we find a few stars (ID 293, 299, 300) with dm ≤ 1 pc within their errors, at tm > 0.6 Myr for future close encounters. Besides that,cadding non-axisymmetric component of Milky Way does not change the results. This suggests that the non-axisymmetric component of Milky Way potential has small effect in perturbing the orbital motion of stars for short timescale. That’s why the values of dm are relatively similar within their errors, for both cases of the Milky Way potential.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".