Haloes at the ragged edge: the importance of the splashback radius
Bibliographic record
Abstract
We have explored the outskirts of dark matter haloes out to 2.5 times the virial radius using a large sample of haloes drawn from Illustris, along with a set of zoom simulations (MUGS). Using these, we make a systematic exploration of the shape profile beyond Rvir. In the mean sphericity profile of Illustris haloes, we identify a dip close to the virial radius, which is robust across a broad range of masses and infall rates. The inner edge of this feature may be related to the virial radius and the outer edge with the splashback radius. Due to the high halo-to-halo variation, this result is visible only on average. However, in four individual haloes in the MUGS sample, a decrease in the sphericity and a subsequent recovery is evident close to the splashback radius. We find that this feature persists for several Gyr, growing with the halo. This feature appears at the interface between the spherical halo density distribution and the filamentary structure in the environment. The shape feature is strongest when there is a high rate of infall, implying that the effect is due to the mixing of accreting and virializing material. The filamentary velocity field becomes rapidly mixed in the halo region inside the virial radius, with the area between this and the splashback radius serving as the transition region. We also identify a long-lasting and smoothly evolving splashback region in the radial density gradient in many of the MUGS haloes.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".