The Evolution of Subhalo Orbits in Galaxy Clusters
Bibliographic record
Abstract
Galaxies and their dark matter structures are commonly found within larger-scale structures called galaxy clusters. These clusters exhibit unique conditions to their constituent galaxies, as they have an elevated density compared to the background Universe. Cluster galaxy motion and evolution are tied very heavily to the properties of the cluster. This is seen observationally through the large fraction of galaxies no longer forming stars found within clusters. The dark matter structures galaxies reside in are also expected to undergo stripping and deformation as they move through the highly dense cluster environment. Models for the cessation of star formation in galaxies and galaxy survival times often use the pericenter as a benchmark for the timing of these processes. Also, galaxy orbits are used to probe the properties of the cluster itself. However, to model this accurately, the exact orbit of these galaxies must be well understood, as the density of the host system does not exactly follow the commonly assumed density profiles. The matter distribution of the hosts is often triaxial and is frequently changed through mergers with other systems. Mergers with other similarly sized clusters are especially likely to change the matter distribution of the host system violently. This should change the orbital path of constituent galaxies and thus change the predictions for galaxy quenching and evolution.\\\\ \n \nIn this work, I aim to probe the validity of a semi-analytical model for the evolution of orbits using only the basic properties of the host system. This is done using dark-matter-only, high-resolution simulations. First, I examine the relationship between the orbital properties at the time of merging to properties of the host system. It is found that the distribution of infall parameters is only reasonably independent of mass ratio and that there is clear evidence that the host's state can significantly influence infall parameters. I then show that predictions about the pericentric passage (radius and timing) using infall orbital parameters are within 25\\% of their predicted values. However, it is found that many orbits are kicked away from their predicted orbit, often up to 70\\% of the orbits that merge at a given time. Also, orbits are often pushed deeper into the host, taking longer to reach the pericenter than predicted. Lastly, I attempt to model the evolution of surviving orbits using the host system's mass assembly history (MAH). It is found that this is not feasible, as the effects of major mergers require more information to model correctly. Thus, a detailed model for the evolution of orbital properties requires more information than the general properties of the host.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".