Bayesian AGN Decomposition Analysis for SDSS Spectra: A Correlation Analysis of [OIII]$\lambda5007$ Outflow Kinematics with AGN and Host Galaxy Properties
Remington O. Sexton, William Matzko, Nicholas Darden, Gabriela, Canalizo, Varoujan Gorjian

TL;DR
This paper introduces BADASS, a Bayesian spectral analysis tool for SDSS spectra, enabling detailed decomposition of AGN and host galaxy features, and applies it to study outflow kinematics and their relation to galaxy properties.
Contribution
The paper presents BADASS, a new open-source Python code for automatic, detailed spectral decomposition of AGN and galaxy spectra using Bayesian methods, suitable for large surveys.
Findings
Confirmed [OIII] core as a surrogate for stellar velocity dispersion.
Found evidence of [OIII] broadening scaling with outflow velocity.
Discovered a plane relation involving sigma_*, [OIII] core dispersion, and outflow dispersion.
Abstract
We present Bayesian AGN Decomposition Analysis for SDSS Spectra (BADASS), an open source spectral analysis code designed for automatic detailed deconvolution of AGN and host galaxy spectra, implemented in Python, and designed for the next generation of large scale surveys. BADASS simultaneously fits all spectral components, including power-law continuum, stellar line-of-sight velocity distribution, FeII emission, as well as forbidden (narrow), permitted (broad), and outflow emission line features, all performed using Markov Chain Monte Carlo to obtain robust uncertainties and autocorrelation analysis to assess parameter convergence. BADASS utilizes multiprocessing for batch fitting large samples of spectra while efficiently managing memory and computation resources and is currently being used in a cluster environment to fit thousands of SDSS spectra. We use BADASS to perform a…
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