The role of LRG1 and LRG2’s monopole in inferring the DESI 2024 BAO cosmology
Bibliographic record
Abstract
ABSTRACT The Dark Energy Spectroscopic Instrument (DESI) collaboration recently released its first year of data (DR1) on baryon acoustic oscillations (BAO) in galaxy, quasar, and Lyman-$\alpha$ forest tracers. When combined with cosmic microwave background (CMB) and Type Ia supernovae (SNIa) data, DESI BAO results suggest potential thawing behaviour in dark energy. Cosmological analyses utilize comoving distances along ($D_\mathrm{ H}$) and perpendicular to ($D_\mathrm{ M}$) the line of sight. Notably, there are $1\sim 2\sigma$ deviations in $D_\mathrm{ M}$ and $D_\mathrm{ H}$ from Planck cosmology values in the luminous red galaxies (LRG) bins LRG1 and LRG2.This study examines the role of LRG1 and LRG2 in diverging DESI 2024 BAO cosmology from Planck cosmology. We use angle-averaged distance $D_\mathrm{ V}$ and the ratio $F_{\rm AP}=D_\mathrm{ M}/D_\mathrm{ H}$, which are more directly related to the measured monopole and quadrupole components of the galaxy power spectrum or correlation function, instead of the officially adopted $D_\mathrm{ M}$ and $D_\mathrm{ H}$. This transformation aims to isolate the influence of monopoles in LRG1 and LRG2 on deviations from $w=-1$. Our findings indicate that removing the $D_\mathrm{ V}$ data point in LRG2 aligns DESI + CMB + SNIa data compilation with $w=-1$ within a $2\sigma$ contour and reduces the $H_0$ discrepancy from the Planck 2018 results from $0.63\sigma$ to $0.31\sigma$. Similarly, excluding the $D_\mathrm{ V}$ data point from LRG1 shifts the $w_0/w_a$ contour toward $w=-1$, although no intersection occurs. This highlights the preference of both LRG1 and LRG2 BAO monopole components for the thawing dark energy model, with LRG2 showing a stronger preference. We provide the $D_\mathrm{ V}$ and $F_{\rm AP}$ data and their covariance alongside this paper.
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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 | 0.003 |
| 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) | 0.002 | 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".