MULTI-WAVELENGTH LENS RECONSTRUCTION of A PLANCK and HERSCHEL-DETECTED STAR-BURSTING GALAXY
Bibliographic record
Abstract
We present a source-plane reconstruction of a Herschel and Planck-detected gravitationally lensed dusty star-forming galaxy (DSFG) at z = 1.68 using Hubble, Submillimeter Array (SMA), and Keck observations. The background submillimeter galaxy (SMG) is strongly lensed by a foreground galaxy cluster at z = 0.997 and appears as an arc with a length of ∼15″ in the optical images. The continuum dust emission, as seen by SMA, is limited to a single knot within this arc. We present a lens model with source-plane reconstructions at several wavelengths to show the difference in magnification between the stars and dust, and highlight the importance of multi-wavelength lens models for studies involving lensed DSFGs. We estimate the physical properties of the galaxy by fitting the flux densities to model spectral energy distributions leading to a magnification-corrected star-formation rate (SFR) of 390 ± 60 M yr-1 and a stellar mass of 1.1 ± 0.4 × 1011 M⊙. These values are consistent with high-redshift massive galaxies that have formed most of their stars already. The estimated gas-to-baryon fraction, molecular gas surface density, and SFR surface density have values of 0.43 ± 0.13, 350 ± 200 pc-2, and M yr-1 kpc-2, respectively. The ratio of SFR surface density to molecular gas surface density puts this among the most star-forming systems, similar to other measured SMGs and local ULIRGs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".