Enhancing DESI DR1 full-shape analyses using HOD-informed priors
Bibliographic record
Abstract
Abstract We present an analysis of DESI Data Release 1 (DR1) that incorporates Halo Occupation Distribution (HOD)-informed priors into Full-Shape (FS) modeling of the power spectrum based on cosmological perturbation theory (PT). By leveraging physical insights from the galaxy-halo connection, these HOD-informed priors on nuisance parameters substantially mitigate projection effects in extended cosmological models that allow for dynamical dark energy. The resulting credible intervals now encompass the posterior maximum from the baseline analysis using gaussian priors, eliminating a significant posterior shift observed in baseline studies. In the ΛCDM framework, a combined DESI DR1 FS information and constraints from the DESI DR1 baryon acoustic oscillations (BAO) — including Big Bang Nucleosynthesis (BBN) constraints and a weak prior on the scalar spectral index — yields Ω m = 0.2994 ± 0.0090 and σ 8 = 0.836 +0.024 -0.027 , representing improvements of approximately 4% and 23% over the baseline analysis, respectively. For the w 0 w a CDM model, our results from various data combinations are highly consistent, with all configurations converging to a region with w 0 > -1 and w a < 0. This convergence not only suggests intriguing hints of dynamical dark energy but also underscores the robustness of our HOD-informed prior approach in delivering reliable cosmological constraints.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".