<i>Planck</i>’s dusty GEMS
Bibliographic record
Abstract
We study the properties of the foreground galaxy of the Ruby, the brightest gravitationally lensed high-redshift galaxy on the sub-millimeter sky as probed by the Planck satellite, and part of our sample of Planck’s dusty GEMS. The Ruby consists of an Einstein ring of 1.4′′ diameter at z = 3.005 observed with ALMA at 0.1′′ resolution, centered on a faint, red, massive lensing galaxy seen with HST/WFC3, which itself has an exceptionally high redshift, z = 1.525 ± 0.001, as confirmed with VLT/X-shooter spectroscopy. Here we focus on the properties of the lens and the lensing model obtained with LENSTOOL. The rest-frame optical morphology of this system is strongly dominated by the lens, while the Ruby itself is highly obscured, and contributes less than 10% to the photometry out to the K band. The foreground galaxy has a lensing mass of (3.70 ± 0.35) × 1011M⊙. Magnification factors are between 7 and 38 for individual clumps forming two image families along the Einstein ring. We present a decomposition of the foreground and background sources in the WFC3 images, and stellar population synthesis modeling with a range of star-formation histories for Chabrier and Salpeter initial mass functions (IMFs). Only the stellar mass range obtained with the latter agrees well with the lensing mass. This is consistent with the bottom-heavy IMFs of massive high-redshift galaxies expected from detailed studies of the stellar masses and mass profiles of their low-redshift descendants, and from models of turbulent gas fragmentation. This may be the first direct constraint on the IMF in a lens at z = 1.5, which is not a cluster central galaxy.
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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 | 0.000 | 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".