<scp>The Three Hundred</scp> project: the gas disruption of infalling objects in cluster environments
Bibliographic record
Abstract
Abstract We analyse the gas content evolution of infalling haloes in cluster environments from The Three Hundred project, a collection of 324 numerically modelled galaxy clusters. The haloes in our sample were selected within 5R200 of the main cluster halo at $z$ = 0 and have total halo mass M200 ≥ 1011h−1M⊙. We track their main progenitors and study their gas evolution since their crossing into the infall region, which we define as 1–4R200. Studying the radial trends of our populations using both the full phase-space information and a line-of-sight projection, we confirm the Arthur et al. (2019) result and identify a characteristic radius around 1.7R200 in 3D and at R200 in projection at which infalling haloes lose nearly all of the gas prior their infall. Splitting the trends by subhalo status,we show that subhaloes residing in group-mass and low-mass host haloes in the infall region follow similar radial gas-loss trends as their hosts, whereas subhaloes of cluster-mass host haloes are stripped of their gas much further out. Our results show that infalling objects suffer significant gaseous disruption that correlates with time-since-infall, cluster-centric distance, and host mass, and that the gaseous disruption they experience is a combination of subhalo pre-processing and object gas depletion at a radius that behaves like an accretion shock.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".