The Nonlinear Evolution of Galaxy Intrinsic Alignments
Bibliographic record
Abstract
The non-Gaussian contribution to the intrinsic halo spin alignments is analytically modeled and numerically detected. Assuming that the growth of non-Gaussianity in the density fluctuations caused the tidal field to have a nonlinear-order effect on the orientations of the halo angular momentum, we model the intrinsic halo spin alignments as a linear scaling of the density correlations on large scales, which is different from the previous quadratic scaling model based on the linear tidal torque theory. Then, we analyze the halo catalogs from the recent high-resolution Millenium Run simulation at four different redshifts ( z = 0, 0.5, 1, and 2) and measure quantitatively the degree of the nonlinear effect on the halo spin alignments and its changes with redshifts. A clear signal of spin correlations is found on scales as large as 10 h −1 Mpc at z = 0, which marks a detection of the nonlinear tidal effect on the intrinsic halo alignments. We also investigate how the nonlinear effect depends on the intrinsic properties of the halos. It is found that the degree of the nonlinear tidal effect increases as the halo mass scale decreases, the halo specific angular momentum increases, and the halo peculiar velocity decreases. We discuss the implications of our results for weak gravitational lensing.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".