Bibliographic record
Abstract
Dark matter makes up around a quarter of the total energy density in the universe, but its identity remains elusive. Current ways of studying dark matter have centered around its macroscopic properties, such as density distribution and large scale structure formation. The halo model of large scale structure is an important tool that cosmologists use to study the phenomenological behaviour and nonlinear evolution of structure in the universe. However, it is well known that there is no simple way to impose conservation laws in the halo model. This can severely impair the predictions on large scales for observables such as weak lensing or the kinematic Sunyaev-Zel’dovich effect, which should satisfy mass and momentum conservation, respectively. For example, the standard halo model overpredicts weak lensing power spectrum by > 8% on scales > 20 degrees. To address this problem, we present an Amended Halo Model, explicitly separating the linear perturbations from compensated halo profiles. This is guaranteed to respect conservation laws, as well as linear theory predictions on large scales. We also provide a simple fitting function for the compensated halo profiles, and discuss the modified predictions for 1- halo and 2-halo terms, as well as other cosmological observations such as weak lensing power spectrum. \nSimilar to previous and recent works centered around the halo model, this work is physically motivated and matches simulation data to a greater degree of accuracy than the standard halo model currently does. We compare our results to previous work, and argue that the amended halo model provides a more efficient and accurate framework to capture physical effects that happen in the process of large scale cosmological structure formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".