MUSEQuBES: characterizing the circumgalactic medium of redshift ≈3.3 Ly α emitters
Bibliographic record
Abstract
ABSTRACT We present the first characterization of the circumgalactic medium of Ly α emitters (LAEs), using a sample of 96 z ≈ 3.3 LAEs detected with the VLT/MUSE in fields centred on eight bright background quasars. The LAEs have low Ly α luminosities ($\sim 10^{42}\, \text{erg}\, \text{s}^{-1}$) and star formation rates (SFRs) $\sim 1~\text{M}_\odot \, \text{yr}^{-1}$, which for main-sequence galaxies correspond to stellar masses of only $\sim 10^{8.6}\, \text{M}_\odot$. The median transverse distance between the LAEs and the quasar sightlines is 165 proper kpc (pkpc). We stacked the high-resolution quasar spectra and measured significant excess H i and C iv absorption near the LAEs out to 500 $\text{km}\, \text{s}^{-1}$ and at least ≈250 pkpc (corresponding to ≈7 virial radii). At $\lesssim 30~\text{km}\, \text{s}^{-1}$ from the galaxies, the median H i and C iv optical depths are enhanced by an order of magnitude. The absorption is significantly stronger around the ≈1/3 of our LAEs that are part of ‘groups’, which we attribute to the large-scale structures in which they are embedded. We do not detect any strong dependence of either the H i or C iv absorption on transverse distance (over the range ≈50–250 pkpc), redshift, or the properties of the Ly α emission line (luminosity, full width at half-maximum, or equivalent width). However, for H i, but not C iv, the absorption at $\lesssim 100\, \text{km}\, \text{s}^{-1}$ from the LAE does increase with the SFR. This suggests that LAEs surrounded by more H i tend to have higher SFRs.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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".