Spatially Extended Low-ionization Emission Regions (LIERs) at z ∼ 0.9
Bibliographic record
Abstract
Abstract We present spatially resolved emission diagnostics for eight z ∼ 0.9 galaxies that demonstrate extended low-ionization emission line regions over kpc scales. Eight candidates are selected based on their spatial extent and emission line fluxes from slitless spectroscopic observations with the Hubble Space Telescope/Wide Field Camera 3 G141 and G800L grisms in the well-studied Great Observatories Origins Deep Survey (GOODS) fields. Five of the candidates (62.5%) are matched to X-ray counterparts in the Chandra X-ray Observatory Deep Fields. We modify the traditional Baldwin–Philips–Terlevich (BPT) emission line diagnostic diagram to use [S ii]/(Hα + [N ii]) instead of [N ii]/Hα to overcome the blending of [N ii] and Hα + [N ii] in the low-resolution slitless grism spectra. We construct emission line ratio maps and place the individual pixels in the modified BPT. The extended low-ionization nuclear emission line regions (LINER)-like emission present in all of our candidates, coupled with X-ray properties consistent with star-forming galaxies and weak [O iii]λ5007 Å detections, is inconsistent with purely nuclear sources (LINERs) driven by active galactic nuclei (AGNs). While recent ground-based integral field unit spectroscopic surveys have revealed significant evidence for diffuse LINER-like emission in galaxies within the local universe (z ∼ 0.04), this work provides the first evidence for the non-AGN origin of LINER-like emission out to high redshifts.
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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 | 0.000 |
| 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) | 0.002 | 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".