Constraining the potential of the Milky Way using stellar streams and the Inverse Time Integration method
Bibliographic record
Abstract
ABSTRACT We develop a method for constraining the potential of the Milky Way using stellar streams with a known progenitor. The method expresses the stream in angle-action coordinates and integrates the orbits of the stars backwards in time to obtain the stripping point positions of the stream stars relative to the cluster. In the potential that generated the stream, the stars return approximately to the cluster centre. In a different potential, they are redirected to different locations. The free parameters of the model are estimated by maximizing the degree of clustering of the stripping point distribution. We test this method with the stellar stream of the globular cluster M68 (NGC 4590). We use an N-body code to simulate the stream and generate a realistic star sample using a model of the Gaia selection function. We also simulate the expected observational uncertainties, and estimate the heliocentric distances and radial velocities of the stream stars from the cluster orbit. Using this sample of stars, we recover the values of four free parameters characterizing the potential of the disc and the dark halo to an accuracy of about 8 per cent of the correct values. We show that this accuracy is improved up to about 2 per cent using the expected end-of-mission Gaia data. In addition, we obtain a strong correlation between the mass of the disc and the dark halo axis ratio, which is explained by the fact that the stream flows close and parallel to the disc plane.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".