<scp>mglens</scp>: Modified gravity weak lensing simulations for emulation-based cosmological inference
Bibliographic record
Abstract
ABSTRACT We present mglens, a large series of modified gravity lensing simulations tailored for cosmic shear data analyses and forecasts in which cosmological and modified gravity parameters are varied simultaneously. Based on the forge and bridgeN-body simulation suites presented in companion papers, we construct 100 × 5000 deg2 of mock Stage-IV lensing data from two 4D Latin hypercubes that sample cosmological and gravitational parameters in f(R) and nDGP gravity, respectively. These are then used to validate our inference analysis pipeline based on the lensing power spectrum, exploiting our implementation of these modified gravity models within the cosmosis cosmological inference package. Sampling this new likelihood, we find that cosmic shear can achieve 95 per cent CL constraints on the modified gravity parameters of log$_{10}[f_{R_0}] \lt $ −4.77 and log10[H0rc] > 0.09, after marginalizing over intrinsic alignments of galaxies and including scales up to ℓ = 5000. We also investigate the impact of photometric uncertainty, scale cuts, and covariance matrices. We finally explore the consequences of analysing mglens data with the wrong gravity model, and report catastrophic biases for a number of possible scenarios. The Stage-IV mglens simulations, the forge and bridge emulators and the cosmosis interface modules will be made publicly available upon journal acceptance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".