<i>SPITZER</i>-IRAC IDENTIFICATION OF<i>HERSCHEL</i>-ATLAS SPIRE SOURCES
Bibliographic record
Abstract
We use Spitzer -IRAC data to identify near-infrared counterparts to submillimeter galaxies detected with Herschel -SPIRE at 250 μm in the Herschel Astrophysical Terahertz Large Area Survey. Using a likelihood ratio analysis we identify 146 reliable IRAC counterparts to 123 SPIRE sources out of the 159 in the survey area. We find that, compared to the field population, the SPIRE counterparts occupy a distinct region of the 3.6 and 4.5 μm color–magnitude space, and we use this property to identify 23 further counterparts to 13 SPIRE sources. The IRAC identification rate of 86% is significantly higher than those that have been demonstrated with wide-field ground-based optical and near-IR imaging of Herschel fields. We estimate a false identification rate of 3.6%, corresponding to 4–5 sources. Among the 73 counterparts that are undetected in Sloan Digital Sky Survey, 57 have both 3.6 and 4.5 μm coverage. Of these, 43 have [3.6] − [4.5] > 0, indicating that they are likely to be at z ≳ 1.4. Thus, ∼40% of identified SPIRE galaxies are likely to be high-redshift ( z ≳ 1.4) sources. We discuss the statistical properties of the IRAC-identified SPIRE galaxy sample including far-IR luminosities, dust temperatures, star formation rates, and stellar masses. The majority of our detected galaxies have 10 10 –10 11 L ☉ total IR luminosities and are not intense starbursting galaxies as those found at z ∼ 2, but they have a factor of 2–3 above average specific star formation rates compared to near-IR selected galaxy samples.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".