The Bound Mass of Substructures in Dark Matter Halos
Bibliographic record
Abstract
We present a new definition of subhalos in dissipationless dark matter N -body simulations, based on the coherent identification of their dynamically bound constituents. Whereas previous methods of determining the energetically bound components of a subhalo ignored the contribution of all the remaining particles in the halo (those not geometrically or dynamically associated with the subhalo), our method allows for all the forces, both internal and external, exerted on the subhalo. We demonstrate, using the output of a simulation at different time steps, that our new method is more accurate at identifying the bound mass of a subhalo. We then compare our new method to previously adopted means of identifying subhalos by applying each to a sample of 1838 virialized halos extracted from a high-resolution cosmological simulation. We find that the subhalo distributions are similar in each case, and that the increase in the binding energy of a subhalo from including all the particles located within it is almost entirely balanced by the losses due to the external forces; the net increase in the mass fraction of subhalos is roughly 10%, and the extra substructures tend to reside in the inner parts of the system. Finally, we compare the subhalo populations of halos to the subsubhalo populations of subhalos, finding the two distributions to be similar. This is a new and interesting result, suggesting a self-similarity in the hierarchy substructures within cluster mass halos.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".