Spatially Resolved Stellar Kinematics of the Ultra-diffuse Galaxy Dragonfly 44. I. Observations, Kinematics, and Cold Dark Matter Halo Fits
Bibliographic record
Abstract
Abstract We present spatially resolved stellar kinematics of the well-studied ultra-diffuse galaxy (UDG) Dragonfly 44, as determined from 25.3 hr of observations with the Keck Cosmic Web Imager. The luminosity-weighted dispersion within the half-light radius is km s−1, lower than what we had inferred before from a DEIMOS spectrum in the Hα region. There is no evidence for rotation, with (90% confidence) along the major axis, in possible conflict with models where UDGs are the high-spin tail of the normal dwarf galaxy distribution. The spatially averaged line profile is more peaked than a Gaussian, with Gauss–Hermite coefficient h 4 = 0.13 ± 0.05. The mass-to-light ratio (M/L) within the effective radius is M ⊙/L ⊙, similar to other UDGs and higher by a factor of six than smaller galaxies of the same luminosity. This difference between UDGs and other galaxies is, however, sensitive to the aperture that is used, and it is much reduced when the M/L ratios are measured within a fixed radius of 10 kpc. Dragonfly 44 has a rising velocity dispersion profile, from km s−1 at R = 0.2 kpc to km s−1 at R = 5.1 kpc. The profile can only be fit with a cuspy Navarro–Frenk–White profile if the orbital distribution has strong tangential anisotropy, with . An alternative explanation is that the dark matter profile has a core: a Di Cintio et al. density profile with a mass-dependent core provides a very good fit to the kinematics for a halo mass of and , i.e., isotropic orbits. This model predicts a slight positive kurtosis, in qualitative agreement with the measured h 4 parameter. UDGs such as Dragonfly 44 are dark matter dominated even in their centers and can constrain the properties of dark matter in a regime where baryons usually dominate the kinematics: small spatial scales in massive halos. In a companion paper we provide constraints on the axion mass in the context of “fuzzy” dark matter models.
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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 | 0.000 |
| 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 | 0.000 | 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".