DES Y1 results: Splitting growth and geometry to test <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi mathvariant="normal">Λ</mml:mi><mml:mi>CDM</mml:mi></mml:math>
Bibliographic record
Abstract
We analyze Dark Energy Survey (DES) data to constrain a cosmological model where a subset of parameters---focusing on ${\mathrm{\ensuremath{\Omega}}}_{m}$---are split into versions associated with structure growth (e.g., ${\mathrm{\ensuremath{\Omega}}}_{m}^{\mathrm{grow}}$) and expansion history (e.g., ${\mathrm{\ensuremath{\Omega}}}_{m}^{\mathrm{geo}}$). Once the parameters have been specified for the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ cosmological model, which includes general relativity as a theory of gravity, it uniquely predicts the evolution of both geometry (distances) and the growth of structure over cosmic time. Any inconsistency between measurements of geometry and growth could therefore indicate a breakdown of that model. Our growth-geometry split approach therefore serves both as a (largely) model-independent test for beyond-$\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ physics, and as a means to characterize how DES observables provide cosmological information. We analyze the same multiprobe DES data as [Phys. Rev. Lett. 122, 171301 (2019)] : DES Year 1 (Y1) galaxy clustering and weak lensing, which are sensitive to both growth and geometry, as well as Y1 BAO and Y3 supernovae, which probe geometry. We additionally include external geometric information from BOSS DR12 BAO and a compressed Planck 2015 likelihood, and external growth information from BOSS DR12 RSD. We find no significant disagreement with ${\mathrm{\ensuremath{\Omega}}}_{m}^{\mathrm{grow}}={\mathrm{\ensuremath{\Omega}}}_{m}^{\mathrm{geo}}$. When DES and external data are analyzed separately, degeneracies with neutrino mass and intrinsic alignments limit our ability to measure ${\mathrm{\ensuremath{\Omega}}}_{m}^{\mathrm{grow}}$, but combining DES with external data allows us to constrain both growth and geometric quantities. We also consider a parametrization where we split both ${\mathrm{\ensuremath{\Omega}}}_{m}$ and $w$, but find that even our most constraining data combination is unable to separately constrain ${\mathrm{\ensuremath{\Omega}}}_{m}^{\mathrm{grow}}$ and ${w}^{\mathrm{grow}}$. Relative to $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$, splitting growth and geometry weakens bounds on ${\ensuremath{\sigma}}_{8}$ but does not alter constraints on $h$.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".