<i>Euclid</i> : Constraints on f(R) cosmologies from the spectroscopic and photometric primary probes
Bibliographic record
Abstract
We forecast the constraints that the Euclid mission will place on the Hu–Sawicki f ( R ) modified gravity model using galaxy clustering and weak lensing observations. Euclid ’s primary probes will provide spectroscopic redshifts, photometric angular clustering, and weak lensing cosmic shear, thus allowing for precise tests of deviations from general relativity. We consider these observables to evaluate how well Euclid can constrain the extended model parameter f R 0 . For a fiducial value of | f R 0 | = 5 × 10 −6 , we find that in our baseline pessimistic setting, Euclid will constrain log 10 | f R 0 | at the 4% level with spectroscopic clustering, at 2.7% with the cross-correlation of photometric probes, and at 1.8% when combining all primary probes. This corresponds to an estimation on this model parameter of approximately | f R 0 = (5.0 +1.2 −0.9 × 10 −6 at the 1 σ level. We also forecast constraints for models with | f R 0 | = 5 × 10 −5 and | f R 0 | = 5 × 10 −7 , finding that Euclid will distinguish these from the standard cosmological model at more than 3 σ when using the full combination of primary probes. Euclid will be a powerful experiment to test modifications to gravity, provided that the theoretical systematics of the non-linear modelling are kept under control.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".