Very Large Telescope Spectroscopy of Ultra-faint Dwarf Galaxies. I. Boötes I, Leo IV, and Leo V
Bibliographic record
Abstract
Abstract We perform consistent reductions and measurements for three ultra-faint dwarf galaxies (UFDs): Boötes I, Leo IV, and Leo V. Using the public archival data from the GIRAFFE spectrograph on the Very Large Telescope (VLT), we locate new members and provide refined measurements of physical parameters for these dwarf galaxies. We identify nine new Leo IV members and four new Leo V members, and perform a comparative analysis of previously discovered members. Additionally, we identify one new binary star in both Leo IV and Leo V. After removing binary stars, we recalculate the velocity dispersions of Boötes I and Leo IV to be 5.1 − 0.8 + 0.7 and 3.4 − 0.9 + 1.3 km s−1, respectively; we do not resolve the Leo V velocity dispersion. We identify a weak velocity gradient in Leo V that is ∼4× smaller than the previously calculated gradient and that has a corresponding position angle that differs from the value in the literature by ∼120°. Combining the VLT data with previous values from the literature, we reanalyze the Boötes I metallicity distribution function and find that a model including infall of pristine gas, while Boötes I was forming stars’ best fits the data. Our analysis of Leo IV, Leo V, and other UFDs will enhance our understanding of these enigmatic stellar populations and contribute to future dark matter studies. This is the first in a series of papers examining 13 UDFs observed with VLT/GIRAFFE between 2009 and 2017. Similar analyses of the remaining 10 UFDs will be presented in forthcoming papers.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".