The Fate of Dwarf Galaxies in Clusters and the Origin of Intracluster Stars. II. Cosmological Simulations
Bibliographic record
Abstract
We combine a N-body simulation algorithm with a subgrid treatment of galaxy formation, mergers, and tidal destruction, and an observed conditional luminosity function Phi(L|M), to study the origin and evolution of galactic and extragalactic light inside a cosmological volume of size (100 Mpc)^3, in a concordance LCDM model. This algorithm simulates the growth of large-scale structures and the formation of clusters, the evolution of the galaxy population in clusters, the destruction of galaxies by mergers and tides, and the evolution of the intracluster light. We find that destruction of galaxies by mergers dominates over destruction by tides by about an order of magnitude at all redshifts. However, tidal destruction is sufficient to produce intracluster light fractions f_ICL that are sufficiently high to match observations. The bulk of the intracluster light (60%) is provided by intermediate galaxies of total masses 10^11 Msun-10^12 Msun and stellar masses 6x10^8 Msun-3x10^10 Msun that were tidally destroyed by even more massive galaxies. The contribution of low-mass galaxies to the intracluster light is small and the contribution of dwarf galaxies is negligible, even though, by numbers, most galaxies that are tidally destroyed are dwarfs. Tracking clusters back in time, we find that their values of f_ICL tend to increase over time, but can experience sudden changes that are sometimes non-monotonic. These changes occur during major mergers involving clusters of comparable masses but very different intracluster luminosities. Most of the tidal destruction events take place in the central regions of clusters. As a result, the intracluster light is more centrally concentrated than the galactic light. Our results support tidal destruction of intermediate-mass galaxies as a plausible scenario for the origin of the intracluster light.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".