Disentangling a group of lensed submm galaxies at z~2.9
Bibliographic record
Abstract
MS$\,$0451.6$-$0305 is a rich galaxy cluster whose strong lensing is particularly prominent at submm wavelengths. We combine new SCUBA-2 data with imaging from Herschel SPIRE and PACS and HST in order to try to understand the nature of the sources being lensed. In the region of the "giant submm arc," we uncover seven multiply imaged galaxies (up from the previously known three), of which six are found to be at a redshift of $z\sim2.9$, and possibly constitute an interacting system. Using a novel forward-modelling approach, we are able to simultaneously deblend and fit SEDs to the individual galaxies that contribute to the giant submm arc, constraining their dust temperatures, far infrared luminosities and star formation rates. The submm arc first identified by SCUBA can now be seen to be composed of at least five distinct sources, four of these within the galaxy group at $z\sim2.9$. The total unlensed luminosity for this galaxy group is $(3.1\pm0.3) \times 10^{12}\,\mathrm{L}_\odot$, which gives an unlensed star formation rate of $(450\pm50)$ M$_\odot$ yr$^{-1}$. From the properties of this system, we see no evidence of evolution towards lower temperatures in the dust temperature versus far-infrared luminosity relation for high redshift galaxies.
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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) | 0.001 | 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 | 0.000 | 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".