Gemini Near Infrared Spectrograph–Distant Quasar Survey: Prescriptions for Calibrating UV-based Estimates of Supermassive Black Hole Masses in High-redshift Quasars (open access)

Gemini Near Infrared Spectrograph–Distant Quasar Survey: Prescriptions for Calibrating UV-based Estimates of Supermassive Black Hole Masses in High-redshift Quasars

Article describes how the most reliable single-epoch supermassive black hole mass (MBH) estimates in quasars are obtained by using the velocity widths of low-ionization emission lines, typically the Hβλ4861 line. The authors find that utilizing both emission lines, where available, reduces the scatter of UV-based MBH estimates by ∼15% when compared to previous studies.
Date: June 13, 2023
Creator: Dix, Cooper; Matthews, Brandon; Shemmer, Ohad; Brotherton, Michael S.; Myers, Adam D.; Andruchow, I. et al.
System: The UNT Digital Library
Gemini Near Infrared Spectrograph–Distant Quasar Survey: Augmented Spectroscopic Catalog and a Prescription for Correcting UV-based Quasar Redshifts (open access)

Gemini Near Infrared Spectrograph–Distant Quasar Survey: Augmented Spectroscopic Catalog and a Prescription for Correcting UV-based Quasar Redshifts

Article describes how quasars at z ≳ 1 most often have redshifts measured from rest-frame ultraviolet emission lines. One of the most common such lines, C ivλ1549, shows blueshifts up to ≈5000 km s−1 and in rare cases even higher. The authors present spectroscopic measurements for 260 sources at 1.55 ≲ z ≲ 3.50 having −28.0 ≲ Mi ≲ − 30.0 mag from the Gemini Near Infrared Spectrograph–Distant Quasar Survey (GNIRS-DQS) catalog, augmenting the previous iteration, which contained 226 of the 260 sources whose measurements are improved upon in this work.
Date: June 13, 2023
Creator: Matthews, Brandon M.; Dix, Cooper; Shemmer, Ohad; Brotherton, Michael S.; Myers, Adam D.; Andruchow, I. et al.
System: The UNT Digital Library
Using Machine Learning to Predict Genes Underlying Differentiation of Multipartite and Unipartite Traits in Bacteria (open access)

Using Machine Learning to Predict Genes Underlying Differentiation of Multipartite and Unipartite Traits in Bacteria

Article describes how, since the discovery of the second chromosome in the Rhodobacter spaeroides 2.4.1 in 1989 and the revelation of gene sequences, multipartite genomes have been reported in over three hundred bacterial species under nine different phyla. In this study, the authors have attempted to leverage machine learning as a means to identify the genetic factors that underlie the differentiation of bacteria with multipartite and unipartite genomes.
Date: November 13, 2023
Creator: Almalki, Fatemah; Sunuwar, Janak & Azad, Rajeev K.
System: The UNT Digital Library