Late time modification of structure growth and the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>8</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> tension
Bibliographic record
Abstract
The ${S}_{8}$ tension between low-redshift galaxy surveys and the primary cosmic microwave background (CMB) signals a possible breakdown of the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model. Recently differing results have been obtained using low-redshift galaxy surveys and the higher redshifts probed by CMB lensing, motivating a possible time-dependent modification to the growth of structure. We investigate a simple phenomenological model in which the growth of structure deviates from the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ prediction at late times, in particular as a simple function of the dark energy density. Fitting to galaxy lensing, CMB lensing, baryon acoustic oscillations, and supernovae datasets, we find significant evidence---$2.5--3\ensuremath{\sigma}$, depending on analysis choices---for a nonzero value of the parameter quantifying a deviation from $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$. The preferred model, which has a slower growth of structure below $z\ensuremath{\sim}1$, improves the joint fit to the data over $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$. While the overall fit is improved, there is weak evidence for galaxy and CMB lensing favoring different changes in the growth of structure.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".