Deep Submillimeter and Radio Observations in the SSA22 Field. I. Powering Sources and the Lyα Escape Fraction of Lyα Blobs
Bibliographic record
Abstract
Abstract We study the heating mechanisms and Lyα escape fractions of 35 Lyα blobs (LABs) at z ≈ 3.1 in the SSA22 field. Dust continuum sources have been identified in 11 of the 35 LABs, all with star formation rates (SFRs) above 100 M ⊙ yr−1. Likely radio counterparts are detected in 9 out of 29 investigated LABs. The detection of submillimeter dust emission is more linked to the physical size of the Lyα emission than to the Lyα luminosities of the LABs. A radio excess in the submillimeter/radio-detected LABs is common, hinting at the presence of active galactic nuclei. Most radio sources without X-ray counterparts are located at the centers of the LABs. However, all X-ray counterparts avoid the central regions. This may be explained by absorption due to exceptionally large column densities along the line-of-sight or by LAB morphologies, which are highly orientation dependent. The median Lyα escape fraction is about 3% among the submillimeter-detected LABs, which is lower than a lower limit of 11% for the submillimeter-undetected LABs. We suspect that the large difference is due to the high dust attenuation supported by the large SFRs, the dense large-scale environment as well as large uncertainties in the extinction corrections required to apply when interpreting optical data.
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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 | 0.000 |
| 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".