The AKARI FU-HYU galaxy evolution program: first results from the GOODS-N field
Bibliographic record
Abstract
The AKARI FU-HYU mission program carried out mid-infrared imaging of several well studied Spitzer fields preferentially selecting fields already rich in multi-wavelength data from radio to X-ray wavelengths filling in the wavelength desert between the Spitzer IRAC and MIPS bands. We present the initial results for the FU-HYU survey in the GOODS-N field. We utilize the supreme multiwavelength coverage in the GOODS-N field to produce a multiwavelength catalogue from infrared to ultraviolet wavelengths, containing more than 4393 sources, including photometric redshifts. Using the FU-HYU catalogue we present colour-colour diagrams that map the passage of PAH features through our observation bands. We find that the longer mid-infrared bands from AKARI (IRC-L18W 18 micron band) and Spitzer (MIPS24 24 micron band) provide an accurate measure of the total MIR emission of the sources and therefore their probable total mid-infrared luminosity. We also find that colours incorporating the AKARI IRC-S11 11 micron band produce a bimodal distribution where an excess at 11 microns preferentially selects moderate redshift star-forming galaxies. These powerful colour-colour diagnostics are further used as tools to extract anomalous colour populations, in particular a population of Silicate Break galaxies from the GOODS-N field showing that dusty starbursts can be selected of specific redshift ranges (z = 1.2–1.6) by mid-infrared drop-out techniques. The FU-HYU catalogue will be made publically available to the astronomical community.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".