The population of Milky Way satellites in the Λ cold dark matter cosmology
Bibliographic record
Abstract
We present a model for the satellites of the Milky Way in which galaxy formation is followed using semi-analytic techniques applied to the six high-resolution N-body simulations of galactic haloes of the Aquarius project. The model, calculated using the galform code, incorporates improved treatments of the relevant physics in the Λ cold dark matter cosmogony, particularly a self-consistent calculation of reionization by ultraviolet (UV) photons emitted by the forming galaxy population, including the progenitors of the central galaxy. Along the merger tree of each halo, the model calculates gas cooling (by Compton scattering off cosmic microwave background photons, molecular hydrogen and atomic processes), gas heating (from hydrogen photoionization and supernova energy), star formation and evolution. The evolution of the intergalactic medium is followed simultaneously with that of the galaxies. Star formation in the more massive progenitor subhaloes is suppressed primarily by supernova feedback, while for smaller subhaloes, it is suppressed primarily by photoionization due to external and internal sources. The model is constrained to match a wide range of properties of the present-day galaxy population as a whole, but at high redshift it requires an escape fraction of UV photons near unity in order to completely reionize the universe by redshift z≳ 8. In the most successful model, the local sources photoionize the pre-galactic region completely by z≃ 10. In addition to the luminosity function of Milky Way satellites, the model matches their observed luminosity–metallicity relation, their radial distribution and the inferred values of the mass within 300 pc, which in the models increase slowly but significantly with luminosity. There is a large variation in satellite properties from halo to halo, with the luminosity function, for example, varying by a factor of ∼2 among the six simulations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".