The miniJPAS survey: Identification and characterization of the emission line galaxies down to<i>z</i>< 0.35 in the AEGIS field
Bibliographic record
Abstract
The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) is expected to map thousands of square degrees of the northern sky with 56 narrowband filters (spectral resolution ofR ∼ 60) in the upcoming years. This resolution allows us to study emission line galaxies (ELGs) with a minimum equivalent width of 10 Å in the Hαemission line for a median signal-to-noise ratio (S/N) of 5. This will make J-PAS a very competitive and unbiased emission line survey compared to spectroscopic or narrowband surveys with fewer filters. The miniJPAS survey covered 1 deg2, and it used the same photometric system as J-PAS, but the observations were carried out with the pathfinder J-PAS camera. In this work, we identify and characterize the sample of ELGs from miniJPAS with a redshift lower than 0.35, which is the limit to which the Hαline can be observed with the J-PAS filter system. Using a method based on artificial neural networks, we detect the ELG population and measure the equivalent width and flux of the Hα, Hβ, [O III], and [N II] emission lines. We explore the ionization mechanism using the diagrams [OIII]/Hβversus [NII]/Hα(BPT) and EW(Hα) versus [NII]/Hα(WHAN). We identify 1787 ELGs (83%) from the parent sample (2154 galaxies) in the AEGIS field. For the galaxies with reliable EW values that can be placed in the WHAN diagram (2000 galaxies in total), we obtained that 72.8 ± 0.4%, 17.7 ± 0.4%, and 9.4 ± 0.2% are star-forming (SF), active galactic nucleus (Seyfert), and quiescent galaxies, respectively. The distribution of EW(Hα) is well correlated with the bimodal color distribution of galaxies. Based on the rest-frame (u − r)–stellar mass diagram, 94% of the blue galaxies are SF galaxies, and 97% of the red galaxies are LINERs or passive galaxies. The nebular extinction and star formation rate (SFR) were computed from the Hαand Hβfluxes. We find that the star formation main sequence is described as log SFR [M⊙ yr−1] = 0.90−0.02+0.02log M⋆[M⊙]−8.85−0.20+0.19and has an intrinsic scatter of 0.20−0.01+0.01. The cosmic evolution of the SFR density (ρSFR) is derived at three redshift bins: 0 < z ≤ 0.15, 0.15 < z ≤ 0.25, and 0.25 < z ≤ 0.35, which agrees with previous results that were based on measurements of the Hαemission line. However, we find an offset with respect to other estimates that were based on the star formation history obtained from fitting the spectral energy distribution of the stellar continuum. We discuss the origin of this discrepancy, which is probably a combination of several factors: the escape of ionizing photons, the SFR tracers, and dust attenuation, among others.
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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 | 0.002 | 0.001 |
| 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) | 0.002 | 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".