REVIEW 3 cited by
Black Hole Mass and Eddington Ratio as Drivers for the Observable Properties of Radio-Loud and Radio-Quiet QSOs
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Recent studies of black holes in the nuclei of both active and normal galaxies have yielded relationships that permit a physical interpretation of the principal components of the spectra of QSOs. It is shown that principal component (or eigenvector) 1 (PC1) is driven predominantly by L/L{Edd}, and principal component 2 (PC2) is driven by luminosity or accretion rate. This results in a PC2 vs. PC1 diagram in which lines of constant black hole mass are diagonal. Using a sample consisting of the low-redshift PG objects supplemented by 46 radio-loud QSOs, it is shown that such a diagram effectively distinguishes radio-loud from radio-quiet as well as demonstrating that both narrow-line Seyfert 1s and broad absorption-line QSOs (BALQSOs) lie at the high L/LEdd} extreme, though these two types of objects are well separated in the PC2 direction. The very few radio-loud BALQSOs known fall in the region expected to be populated by such objects. Finally, a simple picture that ties together physical parameters and classification of AGN is presented.
Forward citations
Cited by 3 Pith papers
-
Discovery of four Narrow-line Type-1 Quasars at z = 2.54 -- 6.06 with Subaru 'Onohi'ula PFS-SSP
Four narrow-line type-1 quasars at z=2.54 to 6.06 were discovered in Subaru PFS data, extending the NLS1 galaxy population to the early universe.
-
AGNFormer I: Reconstruction of AGN spectra using a probabilistic transformer model
An uncertainty-aware transformer reconstructs masked AGN broad lines and spectral halves with 4-16% flux errors and beats eleven purpose-built Lyα-reconstruction algorithms on a blind benchmark.
-
Classifying Radio-Loud and Radio-Quiet Quasars With Novel PCA Based Regression Classifier
A PCA-based balanced logistic regression classifier achieves the highest recall for radio-loud quasars (0.52) among the tested models, but with low precision (0.11) and lower overall accuracy than Random Forest.
Discussion (0). Continue with ORCID to comment.