Spectroscopic Studies of z~5.7 and z~6.5 Galaxies: Implications for Reionization
Bibliographic record
Abstract
The recent development of large, complete samples which identify high-redshift galaxies at z~5.7 and z~6.5 from deep, wide-field surveys provides detailed information on the earliest galaxies, their numbers, spatial and kinematic distributions, and implications for early reionization of the IGM. In this contribution we present results of spectroscopic studies of z~5.7 and z~6.5 galaxies identified from our deep, Lyman alpha narrowband and multicolor surveys conducted with the SuprimeCam mosaic CCD camera on the 8.3-m Subaru telescope and observed with the DEIMOS multi-object spectrograph on Keck. The luminosity function of the z~6.5 galaxies is shown to be similar to the luminosity function of the z~5.7 galaxy samples, suggesting that a substantial star-forming population is already in place at z~6.5. Comparisons of both individual and stacked spectra of galaxies in these two samples show that the Lyman alpha emission profiles, equivalent widths, and continuum break strengths do not substantially change over this redshift interval. The wide-field nature of the surveys also permits mapping the large-scale distribution of the high-redshift galaxies in spatial structures extending across individual SuprimeCam fields (~60 Mpc). Field-to-field variations in the number of objects at z~6.5 may shortly be able to place constraints on the porosity of the reionization boundary.
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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 | 0.001 |
| 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) | 0.001 | 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 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".