Using ground based data as a precursor for <i>Gaia</i> in getting proper motions of satellites
Bibliographic record
Abstract
Abstract We present our effort to measure the proper motions of satellites in the halo of the Milky Way with mainly ground based telescopes as a precursor on what is possible with Gaia. For our first study, we used wide field optical data from the LBT combined with a first epoch of SDSS observations, on the globular cluster Palomar 5 (Pal 5). Since Pal 5 is associated with a tidal stream it is very useful to constrain the shape of the potential of the Milky Way. The motion and other properties of the Pal 5 system constrain the inner halo of the Milky Way to be rather spherical. Further, we combined adaptive optics and HST to get an absolute proper motion of the globular cluster Pyxis. Using the proper motion and the line-of-sight velocity we find that the orbit of Pyxis is rather eccentric with its apocenter at more than 100 kpc and its pericenter at about 30 kpc. The dynamics excludes an association with the ATLAS stream, the Magellanic clouds, and all satellites of the Milky Way at least down to the mass of Leo II. However, the properties of Pyxis, like metallicity and age, point to an origin from a dwarf of at least the mass of Leo II. We therefore propose that Pyxis originated from an unknown relatively massive dwarf galaxy, which is likely today fully disrupted. Assuming that Pyxis is bound to the Milky Way we derive a 68% lower limit on the mass of the Milky Way of 9.5 × 1011 M⊙.
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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 | 0.000 |
| 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".