Dark Energy Survey Year 1 Results: A Precise H0 Estimate from DES Y1, BAO, and D/H Data
Bibliographic record
Abstract
We combine Dark Energy Survey Year 1 clustering and weak lensing data with baryon acoustic oscillations and Big Bang nucleosynthesis experiments to constrain the Hubble constant. Assuming a flat ΛCDM model with minimal neutrino mass (∑mν = 0.06 eV), we find |$H_0=67.4^{+1.1}_{-1.2}\ \rm {km\,\rm s^{-1}\,\rm Mpc^{-1}}$| (68 per cent CL). This result is completely independent of Hubble constant measurements based on the distance ladder, cosmic microwave background anisotropies (both temperature and polarization), and strong lensing constraints. There are now five data sets that: (a) have no shared observational systematics; and (b) each constrains the Hubble constant with fractional uncertainty at the few-per cent level. We compare these five independent estimates, and find that, as a set, the differences between them are significant at the 2.5σ level (χ2/dof = 24/11, probability to exceed = 1.1 per cent). Having set the threshold for consistency at 3σ, we combine all five data sets to arrive at |$H_0=69.3^{+0.4}_{-0.6}\ \rm {km\,\mathrm{ s}^{-1}\,\mathrm{ Mpc}^{-1}}$|.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".