The Number Density of 0.6 <<i>z</i>< 1.7 Mg<scp>ii</scp>Systems from CORALS: Observational Biases at Intermediate Redshift
Bibliographic record
Abstract
The goal of the Complete Optical and Radio Absorption Line System (CORALS) survey is to quantify the potential impact on QSO absorber statistics from dust in intervening galaxies. Dust may introduce a selection bias in surveys that are based on magnitude-limited QSO samples, leading to an underestimate of absorber number density, n ( z ). Here we present the results of the second phase of the CORALS survey, which extends our previous work on z > 1.8 damped Lyα systems (DLAs) to search for strong metal line systems (candidate DLAs) in the range 0.6 < z < 1.7. We have identified 47 Mg II systems with rest-frame equivalent widths EW(Mg λ2796) > 0.3 Å in our sample of 75 radio-selected quasars. The total redshift path covered by the survey is Δ z = 35.2, 58.2, and 63.8 for EW(Mg λ2796) > 0.3, 0.6, and 1.0 Å thresholds, respectively (5 σ). Our principal and most robust result is that the n ( z ) value of low-redshift Mg II systems determined for the CORALS survey is in excellent agreement with that of optically selected, magnitude-limited QSO samples. We use empirically determined Mg II equivalent width statistics to estimate the likely number of DLAs in this sample. The statistically inferred number density of DLAs, n ( z ) = 0.16 , is consistent with other low-redshift samples, although the large 1 σ error bars permit up to a factor of 2.5 more DLAs in CORALS. However, confirmation of the DLA candidates, precise evaluation of their n ( z ) values, and measurement of their H I column densities awaits UV observations with the Hubble Space Telescope . Finally, we report an excess of intermediate-redshift Mg II systems observed toward bright QSOs, which could be due to a lensing amplification bias. However, there is also evidence that this excess could simply be due to more sensitive EW detection limits toward brighter QSOs. We also emphasize that absorber statistics determined from magnitude-limited surveys reach a steady value if the completeness limit is significantly fainter than the fiducial value of the quasar luminosity function.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".