Fast Estimation of Orbital Parameters in Milky Way-like Potentials
Bibliographic record
Abstract
Orbital parameters, such as eccentricity and maximum vertical excursion, of stars in the Milky Way are an important tool for understanding its dynamics and evolution, but calculation of such parameters usually relies on computationally expensive numerical orbit integration. We present and test a fast method for estimating these parameters using an application of the Stäckel fudge, used previously for the estimation of action-angle variables. We show that the method is highly accurate, to a level of <1% in eccentricity, over a large range of relevant orbits and in different Milky Way-like potentials, and demonstrate its validity by estimating the eccentricity distribution of the RAVE-TGAS data set and comparing it with that from orbit integration. Using this method, the orbital characteristics of the ∼7 million Gaia DR2 stars with radial velocity measurements are computed with Monte Carlo sampled errors in ∼116 hours of parallelized cpu time, at a speed that we estimate to be ∼3 to 4 orders of magnitude faster than using numerical orbit integration. We demonstrate using this catalog that Gaia DR2 samples a large range of orbits in the solar vicinity, down to those with r ap ≲ 2.5 kpc, and out to r peri ≳ 13 kpc. We also show that many of the features present in orbital parameter space have a low mean z max , suggesting that they likely result from disk dynamical effects.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".