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Background
Background
Active Galactic Nuclei, or AGN, are among the most massive and energetic objects in the universe and thus are key in understanding galactic morphology. When characterizing an AGN, one of the fundamental values is the AGN's mass.
Methods for obtaining the mass of an AGN are based on the virial theorem and entail obtaining two values.
Rorbit , the distance from the center of the SMBH to the broad line region (BLR). The broad line region of an AGN is a region of gas that emits Doppler-broadened emission lines due to their high velocities and proximity to the ionizing radiation coming from the accretion disk.
Vorbit , the velocity of the gas in the BLR
(Image from Ricci 2011, PhD. Thesis)
The most rigorous of these methods, called reverberation mapping, can take years to obtain the former of these two values for just one object. For this reason, quicker and more automatable (though less rigorous) methods are desirable.
One such method (the one I use) uses a correlation between the monochromatic luminosity at 5100 Å and Rorbit to estimate Rorbit.
Methods
Methods
Beginning in June 2023, I wrote a Python script to estimate black hole masses using SDSS spectral data and a Python package called PyQSOFit detailed inGuo, Shen & Wang (2018). Using PyQSOFit I was able to obtain the FWHM of the Hβ emission line and monochromatic luminosity at 5100 Å for ~5,000 AGN from the catalog detailed in Souchay et al, 2019.
Example of a Type 1 AGN Hβ emmision fit from PyQSOFit
Then, by referring to the empirical relationships detailed in Vestergaard & Peterson, 2006, I estimated the masses for ~5000 black holes. Plotted below are several hundred of these masses against those estimated by Shen et al. The "clean results" refer to those objects with an Hβ FWHM greater than 1,000 km/s (Type 1 AGN) and signal-to-noise ratios greater than 10.
My results vs those in Shen et al. 2011
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