When do cosmic peaks, filaments or walls merge? A theory of critical events in a multi-scale landscape.
Bibliographic record
Abstract
Abstract The merging rate of cosmic structures is computed, relying on the Ansatz that they can be predicted in the initial linear density field from the coalescence of critical points with increasing smoothing scale, used here as a proxy for cosmic time. Beyond the mergers of peaks with saddle points (a proxy for halo mergers), we consider the coalescence and nucleation of all sets of critical points, including wall-saddle to filament-saddle and wall-saddle to minima (a proxy for filament and void mergers respectively), as they impact the geometry of galactic infall, and in particular filament disconnection. Analytical predictions of the one-point statistics are validated against multiscale measurements in 2D and 3D realisations of Gaussian random fields (the corresponding code being available upon request) and compared qualitatively to cosmological N-body simulations at early times (z ≥ 10) and large scales (≥ 5Mpc/h). The rate of filament coalescence is compared to the merger rate of haloes and the two-point clustering of these events is computed, along with their cross-correlations with critical points. These correlations are qualitatively consistent with the preservation of the connectivity of dark matter haloes, and the impact of the large scale structures on assembly bias. The destruction rate of haloes and voids as a function of mass and redshift is quantified down to z = 0 for a ΛCDM cosmology. The one-point statistics in higher dimensions are also presented, together with consistency relations between critical point and critical event counts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".