Prior-Informed AGN-Host Spectral Decomposition Using PyQSOFit

TL;DR

This paper presents an advanced spectral decomposition method for AGN and host galaxy emission, leveraging PCA templates and prior information to improve accuracy, especially in low SNR data, applied to a large quasar sample.

Contribution

The paper introduces a novel prior-informed PCA-based spectral decomposition technique that reduces degeneracy and over-fitting, enabling large-scale analysis of quasar spectra.

Findings

Achieved a 94% success rate in decomposing 76,565 SDSS quasars.

Provided stellar velocity dispersion measurements for 4,137 quasars.

Confirmed the $M_{\rm BH}-\sigma_*$ relation consistent with previous studies.

Abstract

We introduce an improved method for decomposing the emission of active galactic nuclei (AGN) and their host galaxies using templates from principal component analysis (PCA). This approach integrates prior information from PCA with a penalized pixel fitting mechanism which improves the precision and effectiveness of the decomposition process. Specifically, we have reduced the degeneracy and over-fitting in AGN-host decomposition, particularly for those with low signal-to-noise ratios (SNR), where traditional methods tend to fail. By applying our method to 76,565 SDSS Data Release 16 quasars with $z<0.8$, we achieve a success rate of $\approx$ 94%, thus establishing the largest host-decomposed spectral catalog of quasars to date. Our fitting results consider the impact of the host galaxy on the overestimation of the AGN luminosity and black hole mass ($M_{\rm BH}$). Furthermore, we obtained stellar velocity dispersion ($\sigma_*$) measurements for 4,137 quasars. The slope of the $M_{\rm BH}-\sigma_*$ relation in this subsample is generally consistent with previous quasar studies beyond the local universe. Our method provides a robust and efficient approach to disentangle the AGN and host galaxy components across a wide range of SNRs and redshifts.