Mid‐Infrared Spectral Diagnosis of Submillimeter Galaxies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present deep mid-IR spectroscopy with Spitzer of 13 SMGs in the GOODS-N field.We find strong PAH emission in all of our targets, which allows us to measure mid-IR spectroscopic redshifts and place constraints on the contribution from star formation and AGN activity to the mid-IR emission. In the high-S/N composite spectrum, we find that the hot dust continuum from an AGN contributes at most 30% of the mid-IR luminosity. Individually, only 2/13 SMGs have continuum emission dominating the mid-IR luminosity; one of these SMGs, C1, remains undetected in the deep X-ray images but shows a steeply rising continuum in the mid-IR indicative of a Compton-thick AGN. We find that the mid-IR properties of SMGs are distinct from those of 24 μm–selected ULIRGs at z~2; the former are predominantly dominated by star formation, while the latter are a more heterogeneous sample with many showing significant AGN activity.We fit the IRS spectrum and the mid-IR to radio photometry of SMGs with template SEDs to determine the best estimate of the total IR luminosity from star formation. While many SMGs contain an AGN as evinced by their X-ray properties, our multiwavelength analysis shows that the total IR luminosity, L_(IR), in SMGs is dominated by star formation.We find that high-redshift SMGs lie on the relation between L_(IR) and L_(PAH,6.2) (or L_(PAH,7.7) or L_(PAH,11.3))that has been established for local starburst galaxies. This suggests that PAH luminosity can be used as a proxy for the SFR in SMGs. SMGs are consistent with being a short-lived cool phase in a massive merger where the AGN does not appear to have become strong enough to heat the dust and dominate the mid- or far-IR emission.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it