SDSS-V black hole mapper: the index diagram as a tool to disentangle the influence of the host galaxy in quasar spectra
Bibliographic record
Abstract
ABSTRACT We revisit the Quasar Main Sequence (QMS) by investigating the impact of the stellar component from the host galaxy (HG) on the emission line spectra of the active galactic nuclei (AGNs). We first detect spectra with broad emission lines using a line ratio method for a sample of $\sim$3000 high SNR ($>$20) black hole mapper objects (part of the fifth phase of the Sloan Digital Sky Survey). We then built the Index diagram, a novel diagnostic tool using the z-corrected spectra, model-free, designed to easily identify spectra with significant stellar HG contributions and to classify the AGN spectra into three categories based on AGN–HG dominance: HG-dominated (HGD), Intermediate (INT), and AGN-dominated (AGND) sources. A colour-z diagram was used to refine the AGN–HG classification. We subtract the stellar contributions from the HGD and INT spectra before modelling the AGN spectrum to extract the QMS parameters. Our QMS reveals that HGD galaxies predominantly occupy the Population B region with no R$_{\rm {Fe\,{\small II}}}$, with outliers exhibiting R$_{\rm {Fe\,{\small II}}}$ $>$ 1, likely due to HG subtraction residuals and a faint contribution of h$\beta _{\rm BC}$. INT and AGND spectra show similar distributions in the Population A region, while in Population B, a tail of AGND sources becomes apparent. Cross-matching with radio, infrared, and X-ray catalogues, we find that the strongest radio emitters are associated with HGD and INT groups. Strong X-ray emitters are found in INT and AGND sources, also occupying the AGN region in the Wide-field Infrared Survey Explorer colour diagram.
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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 | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".