Transverse velocities and matter gradient correlations: A new signal and a new challenge to moving-lens analyses
Bibliographic record
Abstract
An observer that is moving toward a high-density region sees, on average, a higher matter density and more foreground-emitting sources ahead than behind themselves. Consequently, the average abundance and luminosity of objects producing cosmological signals around an in-falling dark matter halo is larger in the direction of the halo's motion. In this paper, we demonstrate this effect from simulated cosmological maps of the thermal Sunyaev Zel'dovich effect and the cosmic infrared background. We find that, for a wide range of halo masses and redshifts, oriented stacked profiles of these foregrounds show significant, potentially detectable gradients aligned with the transverse velocity of halos. The signal depends on the halo's mass and redshift, as well as the physical properties of the cosmic web surrounding the halos. We show that this signal is sufficiently prominent to be detected in future cosmic microwave background experiments, therefore offering a new window into the study of cosmological structures. We argue that the dipolar morphological structure of this signal, its orientation, as well as its overall large amplitude, constitute a challenge for the detection of the transverse velocity through the study of the moving lens effect for stacked 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".