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Analysis of a Sample of High Redshift, High Luminosity FeLoBAL Quasars

Presentation #337.10 in the session “AGN and Quasars 2”.

Published onJan 11, 2021
Analysis of a Sample of High Redshift, High Luminosity FeLoBAL Quasars

Quasars are particularly luminous Active Galactic Nuclei (AGNs). The light from the central engine is absorbed by gas in our line of sight, producing absorption lines in the spectra. By modeling these spectra, we can derive the physical parameters of the absorbing gas as well as the velocities of quasar outflows, which tell us how powerful the outflow is. FeLoBAL quasars contain high ionization and low ionization broad absorption lines as well as iron lines. Though FeLoBAL quasars comprise less than 2% of the quasar population, they can contain the most powerful outflows, which provide information about galaxy evolution and feedback.

Our group models FeLoBAL quasars using the novel spectral synthesis code SimBAL, which uses Markov Chain Monte Carlo methods to fit a model to the spectra and derive the physical parameters of the absorbing gas from that model. Previously, our group has analyzed a selection of low redshift (0.5 < z < 1.5) quasars with a luminosity range of 45 < logLbol < 47.5 (Choi et al. in prep). Here, we expand our study to include a sample of high redshift (2.1 < z < 2.6), high luminosity (46.5 < logLbol < 47.5) quasars. As higher luminosity quasars are associated with faster outflows and higher redshifts allow us access to shorter wavelengths in the observed-frame optical spectra, we can obtain more information about quasar outflows and physical gas properties.

Using SimBAL, we modeled our objects and extracted their physical parameters, including ionization and density, which we then compared along with the bolometric luminosity logLbol with the outflow velocity. We found that of the objects we currently have analyzed, 40% have significantly powerful outflows (LKE/Lbol > 0.005). We compared high ionization and low ionization line profiles using the derived gas physical parameters from the best fit models, and we also studied the differences between the objects in the low redshift sample and the objects in our sample.


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