Reconciling mass estimates of ultradiffuse galaxies
Bibliographic record
Abstract
The virial masses of ultradiffuse galaxies (UDGs) have been estimated using the kinematics and abundance of their globular cluster populations, leading to disparate results. Some studies conclude that UDGs reside in massive dark matter haloes while van Dokkum et al., controversially, argue for the existence of UDGs with no dark matter at all. Here, we show that these results arise because the uncertainties of these mass estimates have been substantially underestimated. Indeed, applying the same procedure to the well-studied Fornax dwarf spheroidal would conclude that it has an ‘overmassive’ dark halo or, alternatively, that it lacks dark matter. We corroborate our argument with self-consistent mocks of tracers in cosmological haloes, showing that masses from samples with 5 < N < 10 kinematic tracers (assuming no measurement errors) are uncertain by at least an order of magnitude. Finally, we estimate masses of UDGs with Hubble Space Telescope imaging in Coma and show that their recent mass measurements (with adequate uncertainties) are in agreement with those of other dwarfs, such as Fornax. We also provide bias and scatter factors for a range of sample sizes and measurement errors, of wider applicability.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".