The average submillimetre properties of Lyman α blobs at<i>z</i>= 3
Bibliographic record
Abstract
Ly α blobs (LABs) offer insight into the complex interface between galaxies and their circumgalactic medium. Whilst some LABs have been found to contain luminous star-forming galaxies and active galactic nuclei that could potentially power the Ly α emission, others appear not to be associated with obvious luminous galaxy counterparts. It has been speculated that LABs may be powered by cold gas streaming on to a central galaxy, providing an opportunity to directly observe the ‘cold accretion’ mode of galaxy growth. Star-forming galaxies in LABs could be dust obscured and therefore detectable only at longer wavelengths. We stack deep Submillimetre Common User Bolometer Array 2 (SCUBA-2) observations of the Small Selected Area 22h field to determine the average 850 μm flux density of 34 LABs. We measure S850 = 0.6 ± 0.2 mJy for all LABs, but stacking the LABs by size indicates that only the largest third (area ≥1794 kpc2) have a mean detection, at 4.5σ, with S850 = 1.4 ± 0.3 mJy. Only two LABs (1 and 18) have individual SCUBA-2 >3.5σ detections at a depth of 1.1 mJy beam−1. We consider two possible mechanisms for powering the LABs and find that central star formation is likely to dominate the emission of Ly α, with cold accretion playing a secondary role.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".