Investigations of the Inner Dark Matter Density Profiles of Dwarf Galaxies using Multiple Chemodynamical Populations and Rotation Curves.
Bibliographic record
Abstract
The Λ cold dark matter (DM) model successfully explains the distribution of large scale structure and the cosmic microwave background but, there are several problems concerning the distribution of DM on sub-galactic scales. Robust measurements of the distribution of DM in low mass dwarf galaxies are key to understand the true nature of DM. In this work, we present two new techniques for characterizing the kinematics of dispersion supported systems. The first method identifies localized kinematic substructure in line-of-sight velocity data while the second separates global stellar populations utilizing metallicty, line-of-sight velocity, and spatial information. We apply the first method to the dwarf spheroidal galaxy Ursa Minor and find two localized kinematic substructures at high significance. We present new Keck/DEIMOS spectroscopic observations of Ursa Minor, motivated by the previous detection, which form the largest spectroscopic data set of Ursa Minor. With the new data, we identify two chemodynamical stellar population at high significance with distinct kinematic, metallicity, and spatial distributions. By utilizing the dynamics of multiple stellar populations we break halo profile degeneracies and find the DM slope is more consistent with a ‘cored’ halo than a ‘cuspy’ halo. We present a complementary study comparing a large sample of literature rotation curves to dark matter halos influenced by baryonic processes. The analysis suggests that baryonic processes are an inconsistent solution to the ‘core-cusp’ problem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".