Distribution functions for the modelling of accretion remnants in Milky Way-like galaxies: insights from IllustrisTNG
Bibliographic record
Abstract
ABSTRACT We study accretion remnants around Milky Way analogues in the IllustrisTNG simulations to determine how well commonly used distribution functions (DFs) describe their phase-space distributions. We identify 30 Milky Way analogues and 116 remnants from mergers with stellar mass ratios greater than 1:20. Two power density profiles, as well as rotating constant-anisotropy and Osipkov–Merritt DFs, are fit to the remnants. We determine that the remnants are suitable for equilibrium modelling by assessing them in the context of the Jeans equation. Each of the models we consider is reasonably able to fit the stellar remnant energy and angular momentum distribution, as well as the magnitude and shape of velocity dispersion profiles. Case studies matched to two well-known merger remnants in the stellar halo – Gaia-Sausage/Enceladus (GS/E) and Sequoia – are explored in more depth. We find good evidence that remnants with high anisotropy $\beta$, such as GS/E, are better modelled with a superposition of two Osipkov–Merritt DFs than either a constant-anisotropy model or a single Osipkov–Merritt DF. We estimate an Osipkov–Merritt profile with a scale radius between 2 and 4 kpc would be a good first-order representation of GS/E, and comment on existing observational evidence for this as well as studies that could demonstrate it. Overall, we find that DF-based models work well for describing the kinematics of large merger remnants. Our results will be an important reference for future studies that seek to constrain both the spatial and kinematic properties of merger remnants in the Milky Way stellar halo.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".