Measuring PAH Emission in Ultradeep<i>Spitzer</i>IRS Spectroscopy of High‐Redshift IR‐Luminous Galaxies
Bibliographic record
Abstract
The study of the dominant population of high-redshift IR-luminous galaxies (10 11 -10 12 L ☉ at 1 < z < 3), requires observation of sources at the ~0.1 mJy level in the mid-IR. We present the deepest spectra taken to date with the Infrared Spectrograph (IRS) on the Spitzer Space Telescope . We targeted two faint ( f 24 ~ 0.15 mJy) sources in the Southern GOODS field at z = 1.09 and z = 2.69. Spectra of the lower redshift target were taken in the observed-frame 8-21 μm range, while the spectrum of the higher redshift target covered 21-37 μm. We also present the spectra of two secondary sources within the slit. We detect strong PAH emission in all four targets, and compare the spectra to those of local galaxies observed by the IRS. The z = 1.09 source appears to be a typical, star-formation-dominated IR-luminous galaxy, while the z = 2.69 source is a composite source with strong star formation and a prominent AGN. The IRAC colors of this source show no evidence of rest-frame near-IR stellar photospheric emission. We demonstrate that an AGN that contributes only a small (~10%) fraction of the bolometric luminosity can produce enough hot dust emission to overwhelm the near-IR photospheric emission from stars. Such sources would be excluded from photometric surveys that rely on the near-IR bump to identify starbursts, leading to an underestimate of the star formation rate density.
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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) | 0.001 | 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".