NEAR-INFRARED SURVEY OF THE GOODS-NORTH FIELD: SEARCH FOR LUMINOUS GALAXY CANDIDATES AT<i>z</i>≳ 6.5<sup>,</sup>
Bibliographic record
Abstract
We present near-infrared (NIR; J and K s ) survey of the Great Observatories Origins Deep Survey-North (GOODS-N) field. The publicly available imaging data were obtained using the MOIRCS instrument on the 8.2 m Subaru and the WIRCam instrument on the 3.6 m Canada–France–Hawaii Telescope (CFHT). These observations fulfill a serious wavelength gap in the GOODS-N data, i.e., lack of deep NIR observations. We combine the Subaru/MOIRCS and CFHT/WIRCam archival data to generate deep J - and K s -band images, covering the full GOODS-N field (∼169 arcmin 2 ) to an AB magnitude limit of ∼25 mag (3σ). We applied z 850 -band dropout color selection criteria, using the NIR data generated here. We have identified two possible Lyman break galaxy (LBG) candidates at z ≳ 6.5 with J ≲ 24.5. The first candidate is a likely LBG at z ≃ 6.5 based on a weak spectral feature tentatively identified as Lyα line in the deep Keck/DEIMOS spectrum, while the second candidate is a possible LBG at z ≃ 7 based on its photometric redshift. These z 850 -dropout objects, if confirmed, are among the brightest such candidates found so far. At z ≳ 6.5, their star formation rate is estimated as 100–200 M ☉ yr −1 . If they continue to form stars at this rate, they assemble a stellar mass of ∼5 × 10 10 M ☉ after about 400 million years, becoming the progenitors of massive galaxies observed at z ≃ 5. We study the implication of the z 850 -band dropout candidates discovered here, in constraining the bright end of the luminosity function and understanding the nature of high-redshift galaxies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".