STELLAR MASSES AND STAR FORMATION RATES OF LENSED, DUSTY, STAR-FORMING GALAXIES FROM THE SPT SURVEY
Bibliographic record
Abstract
To understand cosmic mass assembly in the universe at early epochs, we primarily rely on measurements of the stellar masses and star formation rates (SFRs) of distant galaxies. In this paper, we present stellar masses and SFRs of six high-redshift (2.8 ≤ z ≤ 5.7) dusty, star-forming galaxies (DSFGs) that are strongly gravitationally lensed by foreground galaxies. These sources were first discovered by the South Pole Telescope (SPT) at millimeter wavelengths and all have spectroscopic redshifts and robust lens models derived from Atacama Large Millimeter/submillimeter Array observations. We have conducted follow-up observations to obtain multi-wavelength imaging data using the Hubble Space Telescope ( HST ), Spitzer , Herschel , and the Atacama Pathfinder EXperiment. We use the high-resolution HST /Wide Field Camera 3 images to disentangle the background source from the foreground lens in Spitzer /IRAC data. The detections and upper limits provide important constraints on the spectral energy distributions (SEDs) for these DSFGs, yielding stellar masses, IR luminosities, and SFRs. The SED fits of six SPT sources show that the intrinsic stellar masses span a range more than one order of magnitude with a median value ∼5 × 10 10 M ⊙ . The intrinsic IR luminosities range from 4 × 10 12 L ⊙ to 4 × 10 13 L ⊙ . They all have prodigious intrinsic SFRs of 510–4800 M ⊙ yr −1 . Compared to the star-forming main sequence (MS), these six DSFGs have specific SFRs that all lie above the MS, including two galaxies that are a factor of 10 higher than the MS. Our results suggest that we are witnessing ongoing strong starburst events that may be driven by major mergers.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".