The mass distribution in the outskirts of clusters of galaxies as a probe of the theory of gravity
Bibliographic record
Abstract
We show that ς, the radial location of the minimum in the differential radial mass profile M′(r) of a galaxy cluster, can probe the theory of gravity. We derived M′(r) of the dark matter halos of galaxy clusters from N-body cosmological simulations that implement two different theories of gravity: standard gravity in the ΛCDM model, and f(R). We extracted 49 169 dark matter halos in 11 redshift bins in the range 0 ≤ z ≤ 1 and in three different mass bins in the range 0.9 < M200c/1014 h−1 M⊙ < 11. We investigated the correlation of ς with the redshift and the mass accretion rate (MAR) of the halos. We show that ς decreases from ∼3R200c to ∼2R200c when z increases from 0 to 1 in the ΛCDM model. At z ∼ 0.1, ς decreases from 2.8R200c to ∼2.5R200c when the MAR increases from ∼104 h−1 M⊙ yr−1 to ∼2 × 105 h−1 M⊙ yr−1. In the f(R) model, ς is ∼15% larger than in ΛCDM. The median test shows that for samples of ≳400 dark matter halos at z ≤ 0.8, ς is able to distinguish between the two theories of gravity with a p-value ≲10−5. Upcoming advanced spectroscopic and photometric programs will allow a robust estimation of the mass profile of enormous samples of clusters up to large clustercentric distances. These samples will allow us to statistically exploit ς as probe of the theory of gravity, which complements other large-scale probes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".