Measuring Dark Matter in Galaxies: The Mass Fraction within Five Effective Radii
Bibliographic record
Abstract
Abstract Large galaxies may contain an “atmosphere” of hot interstellar X-ray gas, and the temperature and radial density profile of this gas can be used to measure the total mass of the galaxy contained within a given radius r. We use this technique for 102 early-type galaxies with stellar masses M ⋆ > 1010 M ⊙, to evaluate the mass fraction of dark matter (DM) within the fiducial radius r = 5r e , denoted f 5 = f DM(5r e ). On average, these systems have a median with a typical galaxy-to-galaxy scatter ±0.15. Comparisons with mass estimates made through the alternative techniques of satellite dynamics (e.g., velocity distributions of globular clusters, planetary nebulae, satellite dwarfs) as well as strong lensing show encouraging consistency over the same range of stellar mass. We find that many of the disk galaxies (S0/SA0/SB0) have a significantly higher mean f 5 than do the pure ellipticals, by Δf 5 ≃ 0.1. We suggest that this higher level may be a consequence of sparse stellar haloes and quieter histories with fewer major episodes of feedback or mergers. Comparisons are made with the Magneticum Pathfinder suite of simulations for both normal and centrally dominant “Brightest Cluster” galaxies. Though the observed data exhibit somewhat larger scatter at a given galaxy mass than do the simulations, the mean level of DM mass fraction for all classes of galaxies is in good first-order agreement with the simulations. Finally, we find that the group galaxies with stellar masses near M ⋆ ∼ 1011 M ⊙ have relatively more outliers at low f 5 than in other mass ranges, possibly the result of especially effective AGN feedback in that mass range leading to expansion of their DM halos.
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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 | 0.002 |
| 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".