Quasar cosmology: dark energy evolution and spatial curvature
Bibliographic record
Abstract
ABSTRACT We analyse some open debates in cosmology in light of the most updated quasar (QSO) sample, covering a wide redshift range up to $\mathit{ z}$ ∼ 7.5, combined with type Ia supernovae (SNe) and baryon acoustic oscillations (BAOs). Indeed, extending the cosmological analyses with high-redshift data is key to distinguishing between different cosmological models that are degenerate at low redshifts, and allowing better constraints on a possible dark energy (DE) evolution. Also, we discuss combinations of BAO, SNe, and QSO data to understand their compatibility and implications for extensions of the standard cosmological model. Specifically, we consider a flat and non-flat ΛCDM cosmology, a flat and non-flat DE model with a constant DE equation of state parameter (w), and four flat DE models with variable w, namely the Chevallier–Polarski–Linder and Jassal–Bagla–Padmanabhan models, and an ‘exponential’, and Barboza–Alcaniz parametrizations. We find that a joint analysis of QSO+SNe with BAO is only possible in the context of a flat Universe. Indeed BAO confirms the flatness condition assuming a curved geometry, whilst SNe + QSO show evidence of a closed space. We also find ΩM,0 = 0.3 in all data sets assuming a flat ΛCDM model. Yet, all the other models show a statistically significant deviation at 2-3 σ with the combined SNe + SO + BAO data set. In the models where DE density evolves with time, SNe + QSO + BAO data always prefer ΩM,0 > 0.3, w0 < −1 and wa > 0. This DE phantom behaviour is mainly driven by SNe + QSO, while BAO are closer to the flat ΛCDM 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 | 0.001 | 0.002 |
| 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".