Large-scale clustering of Lyman α emission intensity from SDSS/BOSS
Bibliographic record
Abstract
We present a tentative detection of the large-scale structure of Ly α emission in the Universe at redshifts z = 2–3.5 by measuring the cross-correlation of Ly α surface brightness with quasars in Sloan Digital Sky Survey/Baryon Oscillation Spectroscopic Survey. We use a million spectra targeting luminous red galaxies at z < 0.8, after subtracting a best-fitting model galaxy spectrum from each one, as an estimate of the high-redshift Ly α surface brightness. The quasar–Ly α emission cross-correlation is detected on scales 1 ∼ 15 h−1 Mpc, with shape consistent with a ΛCDM model with |$\Omega _{\rm m} =0.30^{+0.10}_{-0.07}$|. The predicted amplitude of this cross-correlation is proportional to the product of the mean Ly α surface brightness, 〈μα〉, the amplitude of mass fluctuations and the quasar and Ly α emission bias factors. We infer 〈μα〉 (bα/3) = (3.9 ± 0.9) × 10−21 erg s−1 cm−2 Å−1 arcsec−2, where bα is the Ly α emission bias. If star-forming galaxies dominate this emission, we find ρSFR = (0.28 ± 0.07)(3/bα) yr−1 Mpc−3. For bα = 3, this value is ∼30 times larger than previous estimates from individually detected Ly α emitters, but consistent with the total ρSFR derived from dust-corrected, continuum UV galaxy surveys, if most of the Ly α photons from these galaxies avoid dust absorption and are reemitted after diffusing in large gas haloes. Heating of intergalactic gas by He ii photoionization from quasar radiation or jets may alternatively explain the detected correlation, and cooling radiation from gas in galactic haloes may also contribute. We also detect redshift space anisotropy of the quasar–Ly α emission cross-correlation, finding evidence at the 3.0σ level that it is radially elongated, which may be explained by radiative-transfer effects. Our measurements represent the first application of the intensity mapping technique to optical observations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".