IRAC Excess in Distant Star‐Forming Galaxies: Tentative Evidence for the 3.3 μm Polycyclic Aromatic Hydrocarbon Feature?
Bibliographic record
Abstract
We present evidence for the existence of an IRAC excess in the spectral energy distribution (SED) of five galaxies at 0.6 < z < 0.9 and one galaxy at z = 1.7. These six galaxies, located in the Great Observatories Origins Deep Survey field (GOODS-N), are star-forming since they present strong 6.2, 7.7, and, 11.3 μm polycyclic aromatic hydrocarbon (PAH) lines in their Spitzer IRS mid-infrared spectra. We use a library of templates computed with PEGASE.2 to fit their multiwavelength photometry and derive their stellar continuum. Subtraction of the stellar continuum enables us to detect in five galaxies a significant excess in the IRAC band pass where the 3.3 μm PAH is expected (i.e., IRAC 5.8 μm for the range of redshifts considered here). We then assess if the physical origin of the IRAC excess is due to an obscured active galactic nucleus (AGN) or warm dust emission. For one galaxy evidence of an obscured AGN is found, while the remaining four do not exhibit any significant AGN activity. Possible contamination by warm dust continuum of unknown origin as found in the Galactic diffuse emission is discussed. The properties of such a continuum would have to be different from the local universe to explain the measured IRAC excess, but we cannot definitively rule out this possibility until its origin is understood. Assuming that the IRAC excess is dominated by the 3.3 μm PAH feature, we find good agreement with the observed 11.3 μm PAH line flux arising from the same C-H bending and stretching modes, consistent with model expectations. Finally, the IRAC excess appears to be correlated with the star formation rate in the galaxies. Hence it could provide a powerful diagnostic for measuring dusty star formation in z > 3 galaxies once the mid-infrared spectroscopic capabilities of the James Webb Space Telescope become available.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".