Stellar masses, metallicity gradients, and suppressed star formation revealed in a new sample of absorption selected galaxies
Bibliographic record
Abstract
Context . Absorbing galaxies are selected via the detection of characteristic absorption lines which their gas-rich media imprint in the spectra of distant light-beacons. The proximity of the typically faint foreground absorbing galaxies to bright background sources makes it challenging to robustly identify these in emission, and hence to characterise their relation to the general galaxy population. Aims . We search for emission to confirm and characterise ten galaxies hosting damped, metal-rich quasar absorbers at redshift z < 1. Methods . We identified the absorbing galaxies by matching spectroscopic absorption -and emission redshifts and from projected separations. Combining emission-line diagnostics with existing absorption spectroscopy and photometry of quasar-fields hosting metal-rich, damped absorbers, we compare our new detections with reference samples and place them on scaling relations. Results . We spectroscopically confirm seven galaxies harbouring damped absorbers (a 70% success-rate). Our results conform to the emerging picture that neutral gas on scales of tens of kpc in galaxies is what causes the characteristic H I absorption. Our key results are: (I) Absorbing galaxies with log 10 [ M ⋆ ,( M ⊙ )] ≳ 10 have star formation rates that are lower than predicted for the main sequence of star formation. (II) The distribution of impact parameter with H I column density and with absorption-metallicity for absorbing galaxies at z ∼ 2–3 extends to z ∼ 0.7 and to lower H I column densities. (III) A robust mean metallicity gradient of ⟨Γ⟩ = −0.022 ± 0.001 dex kpc −1 . (IV) By correcting absorption metallicities for ⟨Γ⟩ and imposing a truncation-radius at 12 kpc, absorbing galaxies fall on top of predicted mass-metallicity relations, with a statistically significant decrease in scatter.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".