A Deficit of Dark Matter from Jeans Modeling of the Ultra-diffuse Galaxy NGC 1052-DF2
Bibliographic record
Abstract
Abstract The discovery of the ultra-diffuse galaxy NGC 1052-DF2 and its peculiar population of star clusters has raised new questions about the connections between galaxies and dark matter (DM) halos at the extremes of galaxy formation. In light of debates over the measured velocity dispersion of its star clusters and the associated mass estimate, we constrain mass models of DF2 using its observed kinematics with a range of priors on the halo mass. Models in which the galaxy obeys a standard stellar-halo mass relation are in tension with the data and also require a large central density core. Better fits are obtained when the halo mass is left free, even after accounting for increased model complexity. The dynamical mass-to-light ratio for our model with a weak prior on the halo mass is , consistent with the stellar population estimate for DF2. We use tidal analysis to find that the low-mass models are consistent with the undisturbed isophotes of DF2. Finally, we compare with Local Group dwarf galaxies and demonstrate that DF2 is an outlier in both its spatial extent and its relative DM deficit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".