Hubble flow variations as a test for inhomogeneous cosmology
Bibliographic record
Abstract
Context. Backreactions from large-scale inhomogeneities may provide an elegant explanation for the observed accelerated expansion of the universe without the need to introduce dark energy. Aims. We propose a cosmological test for a specific model of inhomogeneous cosmology, called timescape cosmology. Using large-scale galaxy surveys such as SDSS and 2MRS, we test the variation of expansion expected in the Λ-cold dark matter (Λ-CDM) model versus a more generic differential expansion using our own calibrations of bounds suggested by timescape cosmology. Methods. Our test measures the systematic variations of the Hubble flow towards distant galaxies groups as a function of the matter distribution in the lines of sight to those galaxy groups. We compare the observed systematic variation of the Hubble flow to mock catalogues from the Millennium Simulation in the case of the Λ-CDM model, and a deformed version of the same simulation that exhibits more pronounced differential expansion. Results. We perform a series of statistical tests, ranging from linear regressions to Kolmogorov-Smirnov tests, on the obtained data. They consistently yield results preferring Λ-CDM cosmology over our approximated model of timescape cosmology. Conclusions. Our analysis of observational data shows no evidence that the variation of expansion differs from that of the standard Λ-CDM model.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".