THE COMPLEX PHYSICS OF DUSTY STAR-FORMING GALAXIES AT HIGH REDSHIFTS AS REVEALED BY<i>HERSCHEL</i>AND<i>SPITZER</i>
Bibliographic record
Abstract
We combine far-infrared photometry from Herschel (PEP/HerMES) with deep mid-infrared spectroscopy from Spitzer to investigate the nature and the mass assembly history of a sample of 31 luminous and ultraluminous infrared galaxies ((U)LIRGs) at z ∼ 1 and 2 selected in GOODS-S with 24 μm fluxes between 0.2 and 0.5 mJy. We model the data with a self-consistent physical model (GRASIL) which includes a state-of-the-art treatment of dust extinction and reprocessing. We find that all of our galaxies appear to require massive populations of old (>1 Gyr) stars and, at the same time, to host a moderate ongoing activity of star formation (SFR ⩽ 100 M ☉ yr −1 ). The bulk of the stars appear to have been formed a few Gyr before the observation in essentially all cases. Only five galaxies of the sample require a recent starburst superimposed on a quiescent star formation history. We also find discrepancies between our results and those based on optical-only spectral energy distribution (SED) fitting for the same objects; by fitting their observed SEDs with our physical model we find higher extinctions (by Δ A V ∼ 0.81 and 1.14) and higher stellar masses (by Δlog( M ⋆ ) ∼ 0.16 and 0.36 dex) for z ∼ 1 and z ∼ 2 (U)LIRGs, respectively. The stellar mass difference is larger for the most dust-obscured objects. We also find lower SFRs than those computed from L IR using the Kennicutt relation due to the significant contribution to the dust heating by intermediate-age stellar populations through "cirrus" emission (∼73% and ∼66% of the total L IR for z ∼ 1 and z ∼ 2 (U)LIRGs, respectively).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".