Bibliographic record
Abstract
Aims. We present the results of applying anomaly detection algorithms to a quasar spectroscopic subsample from the SDSS DR16 quasar catalog, covering the redshift range of 1.88 ≤ z ≤ 2.47. Methods. A principal component analysis (PCA) was employed for the dimensionality reduction of the quasar spectra, followed by a hierarchical k-means clustering in a 20-dimensional PCA eigenvector hyperspace. To prevent broad absorption line (BAL) quasars from being identified as the primary anomaly group, we conducted separate analyses on BAL and non-BAL quasars (a.k.a. QSOs), comparing both classes for a clearer identification of other anomalous quasar types. Results. We identified 2066 anomalous quasars, categorized into 10 broadly defined groups. The anomalous groups include: C IV peakers: quasars with extremely strong and narrow C IV emission lines; Excess Si IV emitters: quasars where the Si IV line is as strong as the C IV line; and Si IV deficient anomalies: which exhibit significantly weaker Si IV emission compared to typical quasars. The anomalous nature of these quasars is attributed to lower Eddington ratios for C IV peakers, supersolar metallicity for Excess Si IV emitters, and subsolar metallicity for Si IV deficient anomalies. Additionally, we identified four groups of BAL anomalies: blue BALs, flat BALs, reddened BALs, and FeLoBALs, distinguished primarily by the strength of reddening in these sources. Furthermore, among the non-BAL quasars, we identified three types of reddened anomaly groups classified as heavily reddened, moderately reddened, and plateau-shaped spectrum quasars, each exhibiting varying degrees of reddening. We present the detected anomalies as an accompanying value-added catalog.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".