The unabridged satellite luminosity function of Milky Way-like galaxies in ΛCDM: the contribution of ‘orphan’ satellites
Bibliographic record
Abstract
ABSTRACT We study the abundance, radial distribution, and orbits of luminous satellites in simulations of Milky Way-mass dark haloes in the $\Lambda$cold dark matter (ΛCDM) cosmology. We follow the evolution of a halo from the Aquarius project and the formation of its ‘maximal’ satellite population with the GALFORM semi-analytic model of galaxy formation. This population consists of all subhaloes able to form stars through efficient gas cooling before or after reionization, which effectively selects systems with peak circular velocities exceeding a critical threshold of roughly $\sim 15$–20 km s−1. The total number of luminous satellites is sensitive to the assumed redshift of reionization, but the shape of the GALFORM satellite stellar mass function is robust, peaking at the stellar mass (∼103$\mathrm{ M}_\odot$) of a halo just above the critical threshold. Subhaloes are prone to artificial disruption in the tidal field of the main halo, with the number of surviving satellites increasing with resolution. Even in the highest resolution simulation (Aq-L1, with particle mass $m_{\rm p}\sim 10^3\, \mathrm{ M}_\odot$), a substantial number of satellite subhaloes are disrupted, leaving behind ‘orphan’ galaxies tracked in GALFORM by the subhalo’s most-bound particle before disruption. When orphans are included (and the effects of tidal stripping on stars are neglected), all simulations that adequately resolve the critical threshold yield a converged maximal satellite stellar mass function. Most orphans were accreted early, are found in the central regions of the main halo, and make up roughly half of all satellites in Aq-L1. Taking orphans into account there is no need to populate subhaloes below the critical threshold with satellites to fit the radial distribution of Milky Way satellites, as has been argued in recent work. Our model predicts that orphans dominate the ultrafaint population and that many more satellites with small apocentric radii should be detected in upcoming deep wide-field surveys.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".