Construction of the damped <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow> <mml:mi>Ly</mml:mi> <mml:mi>α</mml:mi> </mml:mrow> </mml:math> absorber catalog for DESI DR2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow> <mml:mrow> <mml:mrow> <mml:mi>Ly</mml:mi> <mml:mi>α</mml:mi> </mml:mrow> </mml:mrow> </mml:mrow> </mml:math> BAO
Bibliographic record
Abstract
We present the Damped Ly α Toolkit for automated detection and characterization of damped Ly α absorbers (DLAs) in quasar spectra. Our method uses quasar spectral templates with and without absorption from intervening DLAs to reconstruct observed quasar forest regions. The best-fitting model determines whether a DLA is present while estimating the redshift and column density. With an optimized quality cut on detection significance ( Δ χ r 2 > 0.03 ), the technique achieves an estimated 80% purity and 79% completeness when evaluated on simulated spectra with S / N > 2 that are free of broad absorption lines (BALs). We provide a catalog containing candidate DLAs from the DLA Toolkit detected in DESI DR1 quasar spectra, of which 21 719 were found in S / N > 2 spectra with predicted log 10 ( N HI ) > 20.3 and detection significance Δ χ r 2 > 0.03 . We compare the Damped Ly α Toolkit to two alternative DLA finders based on a convolutional neural network and Gaussian process models. We present a strategy for combining these three techniques to produce a high-fidelity DLA catalog from DESI DR2 for the Ly α forest baryon acoustic oscillation measurement. The combined catalog contains 41 152 candidate DLAs with log 10 ( N HI ) > 20.3 from quasar spectra with S / N > 2 . We estimate this sample to be approximately 85% pure and 79% complete when BAL quasars are excluded.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.051 |
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".