Disentangling the optical AGN and Host-galaxy luminosity with a probabilistic Flux Variation Gradient

TL;DR

The paper introduces a probabilistic method to accurately separate AGN and host galaxy luminosities in photometric data, improving precision over traditional techniques and aiding large survey analyses.

Contribution

A novel probabilistic Flux Variation Gradient method that effectively disentangles AGN and host galaxy contributions in photometric reverberation mapping data.

Findings

Achieves within 1% accuracy in host flux recovery for suitable light curves.

Outperforms traditional FVG methods in precision and robustness.

Provides open-source Julia package for community use.

Abstract

We present a novel Probabilistic Flux Variation Gradient (PFVG) approach to to separate the contributions of active galactic nuclei (AGN) and host galaxies in the context of photometric reverberation mapping (PRM) of AGN. We explored the ability of recovering the fractional contribution in a model-independent way using the entire set of light curves obtained through different filters and photometric apertures simultaneously. The method is based on the observed bluer when brighter phenomenon that is attributed to the superimposition of a two-component structure; the red host galaxy, which is constant in time, and the varying blue AGN. We describe the PFVG mathematical formalism and demonstrate its performance using simulated light curves and available PRM observations. The new probabilistic approach is able to recover host-galaxy fluxes to within 1% precision as long as the light curves do not show a significant contribution from time delays. This represents a significant improvement with respect to previous applications of the traditional FVG method to PRM data. The proposed PFVG provides an efficient and accurate way to separate the AGN and host-galaxy luminosities in PRM monitoring data. The method will be especially helpful in the case of large upcoming photometric survey telescopes such as the public optical/near-infrared Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory. Finally, we have made the algorithms freely available as part of our Julia PFVG package.