CLEAR: The Ionization and Chemical-enrichment Properties of Galaxies at 1.1 < z < 2.3
Bibliographic record
Abstract
Abstract We use deep spectroscopy from the Hubble Space Telescope Wide-Field-Camera 3 IR grisms combined with broadband photometry to study the stellar populations, gas ionization and chemical abundances in star-forming galaxies at z ∼ 1.1–2.3. The data stem from the CANDELS Ly α Emission At Reionization (CLEAR) survey. At these redshifts, the grism spectroscopy measure the [O II ] λ λ 3727, 3729, [O III ] λ λ 4959, 5008, and H β strong emission features, which constrain the ionization parameter and oxygen abundance of the nebular gas. We compare the line-flux measurements to predictions from updated photoionization models (MAPPINGS V; Kewley et al.), which include an updated treatment of nebular gas pressure, log P / k = n e T e . Compared to low-redshift samples ( z ∼ 0.2) at fixed stellar mass, log M * / M ⊙ = 9.4–9.8, the CLEAR galaxies at z = 1.35 (1.90) have lower gas-phase metallicity, Δ ( log Z ) = 0.25 (0.35) dex, and higher ionization parameters, Δ ( log q ) = 0.25 (0.35) dex, where U ≡ q / c . We provide updated analytic calibrations between the [O III ], [O II ], and H β emission-line ratios, metallicity, and ionization parameter. The CLEAR galaxies show that at fixed stellar mass, the gas ionization parameter is correlated with the galaxy specific star formation rates, where Δ log q ≃ 0.4 × Δ ( log sSFR ) , derived from changes in the strength of galaxy H β equivalent width. We interpret this as a consequence of higher gas densities, lower gas covering fractions, combined with a higher escape fraction of H-ionizing photons. We discuss both tests to confirm these assertions and implications this has for future observations of galaxies at higher redshifts.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".