Model-independent test for gravity using intensity mapping and galaxy clustering
Bibliographic record
Abstract
We propose a novel method to measure the ${E}_{G}$ statistic from clustering alone. The ${E}_{G}$ statistic provides an elegant way of testing the consistency of General Relativity by comparing the geometry of the Universe, probed through gravitational lensing, with the motion of galaxies in that geometry. Current ${E}_{G}$ estimators combine galaxy clustering with gravitational lensing, measured either from cosmic shear or from CMB lensing. In this paper, we construct a novel estimator for ${E}_{G}$, using only clustering information obtained from two tracers of the large-scale structure: intensity mapping and galaxy clustering. In this estimator, both the velocity of galaxies and gravitational lensing are measured through their impact on clustering. We show that with this estimator, we can suppress the contaminations that affect other ${E}_{G}$ estimators and consequently test the validity of General Relativity robustly. We forecast that with the coming generation of surveys like HIRAX and Euclid, we will measure ${E}_{G}$ with a precision of up to 7% (3.9% for the more futuristic SKA2).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".