SUPERMASSIVE BLACK HOLE MASS ESTIMATES USING SLOAN DIGITAL SKY SURVEY QUASAR SPECTRA AT 0.7 < <i>z</i> < 2
Bibliographic record
Abstract
We present Mg ii -based black hole (BH) mass estimates for 27,602 quasars with rest-frame UV spectra available in the Sloan Digital Sky Survey Data Release Three. This estimation is possible due to the existence of an empirical correlation between the radius of the broad-line region (BLR) and the continuum luminosity at 3000 Å. We regenerate this correlation by applying our measurement method to UV spectra of low-redshift quasars in the Hubble Space Telescope / International Ultraviolet Explorer databases which have corresponding reverberation mapping estimates of the Hβ BLR's radius. Our mass estimation method uses the line dispersion rather than the full width at half-maximum of the low-ionization Mg ii emission line. We measure Mg ii line dispersions for quasars whose spectra have been reconstructed using the most significant eigenspectra produced through Principal Component Analysis. We have tested the reliability of using reconstructed spectra in BH mass estimation using a Monte Carlo simulation and by comparing the results from original and reconstructed Data Release Three spectra. We show that using reconstructed spectra not only makes bias-free mass estimation possible for quasars with low spectroscopic signal-to-noise ratio, but also reduces the intrinsic scatter of the distribution of the BH masses to lower than 0.15 dex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".