The life and times of dark matter haloes: what will I be when I grow up?
Bibliographic record
Abstract
ABSTRACT Are the most massive objects in the Universe today the direct descendants of the most massive objects at higher redshift? We address this question by tracing the evolutionary histories of haloes in the Multi-Dark Planck2 simulation. By following the 100 most massive haloes at $z = 0$ across cosmic time, we find that only 40 per cent of them were among the largest 100 haloes at $z = 1$. This suggests that many of today’s most massive clusters were not the most dominant structures at earlier times, while some of the most massive objects at high redshift do not remain in the top mass ranks at later epochs. The hierarchical nature of structure formation predicts that, on average, massive haloes grow over time, with their abundance in comoving space decreasing rapidly at higher redshifts. However, individual clusters exhibit diverse evolutionary paths: some undergo early rapid growth, while others experience steady accretion or significant merger-driven mass changes. A key assumption in self-similar models of cluster evolution is that the most massive objects maintain their rank in the mass hierarchy across cosmic time. In this work, we test this assumption by constructing a mass-complete sample of haloes within the $(1 {{h^{-1}{\rm Gpc}}})^3$ volume of Multi-Dark and analysing when clusters enter and exit a high-mass-selected sample. Our results demonstrate that cluster selections must be carefully constructed, as significant numbers of objects can enter and leave the sample over time. These findings have important implications for observational cluster selection and comparisons between simulations and surveys, especially at high redshift.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".