Photometric redshifts in the North Ecliptic Pole Wide field based on a deep optical survey with Hyper Suprime-Cam
Bibliographic record
Abstract
ABSTRACT The AKARI space infrared telescope has performed near-infrared to mid-infrared (MIR) observations on the North Ecliptic Pole Wide (NEPW) field (5.4 deg2) for about 1 yr. AKARI took advantage of its continuous nine photometric bands, compared with NASA's Spitzer and Wide-field Infrared Survey Explorer(WISE) space telescopes, which had only four filters with a wide gap in the MIR. The AKARI NEPW field lacked deep and homogeneous optical data, limiting the use of nearly half of the IR sources for extragalactic studies, because of the absence of photometric redshift (photo-z). To remedy this, we have recently obtained deep optical imaging over the NEPW field with five bands (g, r, i, z and Y) of the Hyper Suprime-Camera (HSC) on the Subaru 8-m telescope. We optically identify AKARI-IR sources along with supplementary Spitzer and WISE data as well as pre-existing optical data. In this work, we derive new photo-z using a χ2 template-fitting method code, PHotometric Analysis for Redshift Estimate (Le Phare) and reliable photometry from 26 selected filters including HSC, AKARI, Canada–France–Hawaii Telescope, Maidanak, Kitt Peak National Observatory, Spitzer and WISE data. We take 2026 spectroscopic redshifts (spec-z) from all available spectroscopic surveys over the NEPW field to calibrate and assess the accuracy of the photo-z. At z < 1.5, we achieve a weighted photo-z dispersion of σΔz/(1+z) = 0.053 with η = 11.3 per cent catastrophic errors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".