Constraining modified gravity and growth with weak lensing
Bibliographic record
Abstract
The idea that we live in a Universe undergoing a period of acceleration is a new, yet strongly held, notion in cosmology. As this can, potentially, be explained with a modification to general relativity, we look at current cosmological data with the purpose of testing gravity. First, we constrain a phenomenological model [modified Dvali Gabadadze Porrati (mDGP)] motivated by a possible extra dimension. This is characterized by a parameter a which interpolates between alpha = 0 [lambda cold dark matter (LCDM)] and alpha = 1 (the DGP model). In addition, we analyse general signatures of modified gravity given by the growth parameter gamma and power spectrum parameter Sigma. We utilize large angular scale (theta > 30 arcmin) weak lensing data (Canada-France-Hawaii Telescope Legacy Survey wide) in order to work in the more linear regime and then add, in combination, baryon acoustic oscillations (B Lambda Os) and supernovae. We subsequently show that current weak-lensing data are not yet capable of constraining either model in isolation. However, we demonstrate that even at present this probe is highly beneficial, for in combination with BAOs and Supernovae we obtain alpha < 0.58 and 0.91 at 1 sigma and 2 sigma, respectively. Without the lensing data, no constraint is possible. This corresponds to a disfavouring of the flat DGP braneworld model at over 2 sigma. We highlight these are insensitive to potential systematics in the lensing data such as an underestimation of the shear at high redshift. For the growth signature gamma, we show that, in combination, these probes do not yet have sufficient constraining power. Finally, we look beyond these present capabilities and demonstrate that Euclid, a future weak-lensing survey, will deeply probe the nature of gravity. A 1 sigma error of 0.104 is found for alpha (l(max) = 500) whereas for the general modified signatures we forecast 1 sigma errors of 0.045 for gamma and 0.25 for Sigma(0)(l(max) = 500), which is further tightened to 0.038 for gamma and 0.069 for Sigma(0)(l(max) = 10 000).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".