An efficient technique for pre-selecting low-redshift damped Ly systems
Bibliographic record
Abstract
The number of z ~ 1 damped Lyman alpha systems (DLAs, log N(HI) >= 20.3) per unit redshift is approximately 0.1, making them relatively rare objects. Large, blind QSO surveys for low redshift DLAs are therefore an expensive prospect for space-borne UV telescopes. Increasing the efficiency of these surveys by pre-selecting DLA candidates based on the equivalent widths of metal absorption lines has previously been a successful strategy. However, the success rate of DLA identification is still only ~ 35% when simple equivalent width cut-offs are applied, the majority of systems having 19.0 < log N(HI)<20.3. Here we propose a new way to pre-select DLA candidates. Our technique requires high-to-moderate resolution spectroscopy of the MgII 2796 transition, which is easily accessible from the ground for 0.2 < z < 2.4. We define the D-index, the ratio of the line's equivalent width to velocity spread and measure this quantity for 19 DLAs and 8 sub-DLAs in archival spectra obtained with echelle spectrographs. For the majority of absorbers, there is a clear distinction between the D-index of DLAs compared with sub-DLAs (Kolmogorov-Smirnov probability = 0.8%). Based on this pilot data sample, we find that the D-index can select DLAs with a success rate of up to 90%, an increase in selection efficiency by a factor of 2.5 compared with a simple equivalent width cut. We test the applicability of the D-index at lower resolution and find that it remains a good discriminant of DLAs for FWHM < 1.5 A. However, the recommended D-index cut-off between DLAs and sub-DLAs decreases with poorer resolution and we tabulate the appropriate D-index values that should be used with spectra of different resolutions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".