GOODS-<i>HERSCHEL</i>: IMPACT OF ACTIVE GALACTIC NUCLEI AND STAR FORMATION ACTIVITY ON INFRARED SPECTRAL ENERGY DISTRIBUTIONS AT HIGH REDSHIFT
Bibliographic record
Abstract
We explore the effects of active galactic nuclei (AGN) and star formation activity on the infrared (0.3-1000 microns) spectral energy distributions of luminous infrared galaxies from z = 0.5 to 4.0. We have compiled a large sample of 151 galaxies selected at 24 microns (S24 > 100 uJy) in the GOODS-N and ECDFS fields for which we have deep Spitzer IRS spectroscopy, allowing us to decompose the mid-IR spectrum into contributions from star formation and AGN activity. A significant portion (~25%) of our sample is dominated by an AGN in the mid-IR. Based on the mid-IR classification, we divide our full sample into four sub-samples: z~1 star-forming (SF) sources; z~2 SF sources; AGN with clear 9.7 micron silicate absorption; and AGN with featureless mid-IR spectra. From our large spectroscopic sample and wealth of multi-wavelength data, including deep Herschel imaging at 100, 160, 250, 350, and 500 microns, we use 95 galaxies with complete spectral coverage to create a composite spectral energy distribution (SED) for each sub-sample. We then fit a two-temperature component modified blackbody to the SEDs. We find that the IR SEDs have similar cold dust temperatures, regardless of the mid-IR power source, but display a marked difference in the warmer dust temperatures. We calculate the average effective temperature of the dust in each sub-sample and find a significant (~20 K) difference between the SF and AGN systems. We compare our composite SEDs to local templates and find that local templates do not accurately reproduce the mid-IR features and dust temperatures of our high redshift systems. High redshift IR luminous galaxies contain significantly more cool dust than their local counterparts. We find that a full suite of photometry spanning the IR peak is necessary to accurately account for the dominant dust temperature components in high redshift IR luminous galaxies.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".