Preprocessing, mass-loss and mass segregation of galaxies in dark matter simulations
Bibliographic record
Abstract
We investigate the mass-loss of galaxies in groups and clusters with high-resolution dark matter simulations. We detect weak mass segregation in the inner regions of group/cluster haloes, consistent with observational findings. This applies to samples of galaxy analogues selected using either their present-day mass or past maximum (peak) mass. We find a strong radial trend in the fractional mass lost by the galaxies since peak, independent of their mass. This suggests that segregation is due to massive galaxies having formed closer to the halo centres and not the preferential destruction of smaller galaxies near halo centres. We divide our sample into galaxies that were accreted as a group versus as a single, distinct halo. We find strong evidence for preprocessing – the grouped galaxies lose ∼35–45 per cent of their peak mass before being accreted on to their final host haloes, compared to single galaxies that lose ∼12 per cent. After accretion, however, the single galaxies lose more mass compared to the grouped ones. These results are consistent with a scenario in which grouped galaxies are preprocessed in smaller haloes while single galaxies ‘catch up’ in terms of total mass-loss once they are accreted on to the final host halo. The fractional mass-loss is mostly independent of the galaxy mass and host mass, and increases with amount of time spent in a dense environment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".