Measuring σ <sub>8</sub> using DESI Legacy Imaging Surveys Emission-Line galaxies and Planck CMB lensing, and the impact of dust on parameter inference
Bibliographic record
Abstract
Abstract Measuring the growth of structure is a powerful probe for studying the dark sector, especially in light of the σ 8 tension between primary CMB anisotropy and low-redshift surveys. This paper provides a new measurement of the amplitude of the matter power spectrum, σ 8 , using galaxy-galaxy and galaxy-CMB lensing power spectra of Dark Energy Spectroscopic Instrument Legacy Imaging Surveys Emission-Line Galaxies and the Planck 2018 CMB lensing map. We create an ELG catalog composed of 24 million galaxies and with a purity of 85%, covering a redshift range 0 < z < 3, with z mean = 1.09. We implement several novel systematic corrections, such as jointly modeling the contribution of imaging systematics and photometric redshift uncertainties to the covariance matrix. We also study the impacts of various dust maps on cosmological parameter inference. We measure the cross-power spectra over f sky = 0.25 with a signal-to-background ratio of up to 30 σ . We find that the choice of dust maps to account for imaging systematics in estimating the ELG overdensity field has a significant impact on the final estimated values of σ 8 and Ω M , with far-infrared emission-based dust maps preferring σ 8 to be as low as 0.702 ± 0.030, and stellar-reddening-based dust maps preferring as high as 0.719 ± 0.030. The highest preferred value is at ∼ 3 σ tension with the Planck primary anisotropy results. These findings indicate a need for tomographic analyses at high redshifts and joint modeling of systematics.
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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 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".