<i>SPITZER</i>IMAGING OF STRONGLY LENSED<i>HERSCHEL</i>-SELECTED DUSTY STAR-FORMING GALAXIES
Bibliographic record
Abstract
We present the rest-frame optical spectral energy distribution (SED) and stellar masses of six Herschel -selected gravitationally lensed dusty, star-forming galaxies (DSFGs) at 1 < z < 3. These galaxies were first identified with Herschel /SPIRE imaging data from the Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS) and the Herschel Multi-tiered Extragalactic Survey (HerMES). The targets were observed with Spitzer /IRAC at 3.6 and 4.5 μ m. Due to the spatial resolution of the IRAC observations at the level of 2'', the lensing features of a background DSFG in the near-infrared are blended with the flux from the foreground lensing galaxy in the IRAC imaging data. We make use of higher resolution Hubble /WFC3 or Keck/NIRC2 Adaptive Optics imaging data to fit light profiles of the foreground lensing galaxy (or galaxies) as a way to model the foreground components, in order to successfully disentangle the foreground lens and background source flux densities in the IRAC images. The flux density measurements at 3.6 and 4.5 μ m, once combined with Hubble /WFC3 and Keck/NIRC2 data, provide important constraints on the rest-frame optical SED of the Herschel -selected lensed DSFGs. We model the combined UV- to millimeter-wavelength SEDs to establish the stellar mass, dust mass, star formation rate, visual extinction, and other parameters for each of these Herschel -selected DSFGs. These systems have inferred stellar masses in the range 8 × 10 10 –4 × 10 11 M ⊙ and star formation rates of around 100 M ⊙ yr −1 . This puts these lensed submillimeter systems well above the SFR- M * relation observed for normal star-forming galaxies at similar redshifts. The high values of SFR inferred for these systems are consistent with a major merger-driven scenario for star formation.
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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 | 0.000 |
| 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 | 0.000 |
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".