The miniJPAS and J-NEP surveys: Identification and characterization of the Ly<i>α</i> emitter population and the Ly<i>α</i> luminosity function at redshift 2.05 < <i>z</i> < 3.75
Bibliographic record
Abstract
We present the Lyman-α (Lyα) luminosity function (LF) at 2.05 < z < 3.75, estimated from a sample of 67 Lyα-emitter (LAE) candidates in the Javalambre Physics of the Accelerating Universe Astronomical Survey (J-PAS) pathfinder surveys: miniJPAS and J-NEP. These two surveys cover a total effective area of ∼1.14 deg2 with 54 narrow band (NB) filters (FWHM ∼ 145 Å) across the optical range, with typical limiting magnitudes of ∼23. This set of NBs allowed us to probe Lyα emission in a wide and continuous range of redshifts. We developed a method for detecting Lyα emission for the estimation of the Lyα LF using the whole J-PAS filter set. We tested this method by applying it to the miniJPAS and J-NEP data. In order to compute the corrections needed to estimate the Lyα LF and to test the performance of the candidate selection method, we built mock catalogs. These include representative populations of LAEs at 1.9 < z < 4.5 as well as their expected contaminants, namely low-z galaxies and z < 2 quasi-stellar objects (QSOs). We show that our method is able to provide the Lyα LF at the intermediate-bright range of luminosity (43.5 ≲ log10(LLyα/erg s−1) ≲ 44.5) combining both miniJPAS and J-NEP. The photometric information provided by these surveys suggests that our samples are dominated by bright, Lyα-emitting active galactic nuclei (i.e., AGNs). At log10(LLyα/erg s−1) < 44.5, we fit our Lyα LF to a power law with a slope of A = 0.70 ± 0.25. We also fit a Schechter function to our data, obtaining the following: log10(Φ∗/Mpc−3) = −6.30−0.70+0.48, log10(L∗/erg s−1) = 44.85−0.32+0.50, and α = −1.65−0.27+0.29. Overall, our results confirm the presence of an AGN component at the bright end of the Lyα LF. In particular, we find no significant contribution of star-forming LAEs to the Lyα LF at log10(LLyα/erg s−1) > 43.5. This work serves as a proof of concept for the results that can be obtained with the upcoming data releases of the J-PAS survey.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".