The DEEP2 Redshift Survey: Lyα Emitters in the Spectroscopic Database
Bibliographic record
Abstract
We present the first results of a search for Lyα emitters (LAEs) in the DEEP2 spectroscopic database that uses a search technique that is different from but complementary to traditional narrowband imaging surveys. We have visually inspected ~20% of the available DEEP2 spectroscopic data and have found nine high-quality LAEs with clearly asymmetric line profiles and an additional 10 objects of lower quality, some of which may also be LAEs. Our survey is most sensitive to LAEs at z = 4.4–4.9 and that is indeed where all but one of our high-quality objects are found. We find the number density of our spectroscopically discovered LAEs to be consistent with those found in narrowband imaging searches. The combined, averaged spectrum of our nine high-quality objects is well fit by a two-component model, with a second, lower amplitude component redshifted by ~420 km s −1 with respect to the primary Lyα line, consistent with large-scale outflows from these objects. We conclude by discussing the advantages and future prospects of blank-sky spectroscopic surveys for high- z LAEs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".