Tidal features of classical Milky Way satellites in a Λ cold dark matter universe
Bibliographic record
Abstract
We use the APOSTLE (A Project Of Simulating The Local Environment) cosmological hydrodynamic simulations to examine the effects of tidal stripping on cold dark matter subhaloes that host three of the most luminous Milky Way dwarf satellite galaxies: Fornax, Sculptor and Leo I. We identify simulated satellites that match the observed spatial and kinematic distributions of stars in these galaxies, and track their evolution after infall. We find ∼30 per cent of subhaloes hosting satellites with present-day stellar mass 106–108 M⊙ experience >20 per cent stellar mass-loss after infall. Fornax analogues have earlier infall times compared to Sculptor and Leo I analogues. Star formation in Fornax analogues continues for ∼3–6 Gyr after infall, whereas Sculptor and Leo I analogues stop forming stars <2–3 Gyr after infall. Fornax analogues typically show more significant stellar mass-loss and exhibit stellar tidal tails, whereas Sculptor and Leo I analogues, which are more deeply embedded in their host dark matter haloes at infall, do not show substantial mass-loss due to tides. When additionally comparing the orbital motion of the host subaloes to the measured proper motion of Fornax, we find the matching more difficult; host subhaloes tend to have pericentres smaller than that measured for Fornax itself. From the kinematic and orbital data, we estimate that Fornax has lost 10–20 per cent of its infall stellar mass. Our best estimate for the surface brightness of a stellar tidal stream associated with Fornax is Σ ∼ 32.6 mag arcsec−2, which may be detectable with deep imaging surveys such as DES and LSST.
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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 | 0.000 |
| 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".