REVISITING THE INFLUENCE OF UNIDENTIFIED BINARIES ON VELOCITY DISPERSION MEASUREMENTS IN ULTRA-FAINT STELLAR SYSTEMS
Bibliographic record
Abstract
Velocity dispersion measurements of recently discovered Milky Way satellites with M V ≳ −7 imply that they posses high mass-to-light ratios. The expected velocity dispersions due to their baryonic mass are ∼0.2 km s −1 , but values ≳3 km s −1 are measured. We perform Monte Carlo simulations of mock radial velocity measurements of these systems assuming that they have mass-to-light ratios similar to globular clusters and posses an unidentified binary star population, to determine if these stars could boost the velocity dispersion to the observed values. We find that this hypothesis is unlikely to produce dispersions much in excess of ∼4.5 km s −1 , in agreement with previous work. However, for the systems with the potentially smallest velocity dispersions, values consistent with observations are produced in 5%–40% of our simulations for binary fractions in excess of f bin ( P ⩽ 10 yr)∼ 5%. This sample includes the dwarf galaxy candidates that lie closest to classical globular clusters in M V − r h space. Considered as a population, it is unlikely that all of these dwarf galaxy candidates have mass-to-light ratios typical of globular clusters, but boosting of the observed dispersion by binaries from near-zero values cannot be ruled out at high confidence for several individual dwarf galaxy candidates. Given the importance of obtaining accurate velocity dispersions and dynamical masses for the faintest satellites, it is clearly desirable to directly exclude the possible effect of binaries on these systems. This requires multi-epoch radial velocity measurements with individual uncertainties of ≲1 km s −1 to identify spectroscopic binaries with orbital velocities of the order of the observed velocity dispersion.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".