Optical Fe II and near-infrared Ca II emission in active galaxies

TL;DR

This paper studies optical Fe II and near-infrared Ca II emission lines in active galaxies, exploring their correlation, origin, and potential as tracers for black hole and BLR properties using advanced data analysis techniques.

Contribution

It advances understanding of the connection between Fe II and Ca II emissions and their potential to improve AGN classification and parameter estimation.

Findings

Strong correlation between Fe II and Ca II emission strengths.

tSNE reveals clustering related to emission line properties.

Potential of Ca II as a tracer for AGN accretion rates.

Abstract

The CaFe Project involves the study of the properties of the low ionization emission lines (LILs) pertaining to the broad-line region (BLR) in active galaxies. These emission lines, especially the singly-ionized iron (Fe II) in the optical and the corresponding singly-ionized calcium (Ca II) in the near-infrared (NIR) are found to show a strong correlation in their emission strengths, i.e., with respect to the broad H$\beta$ emission line, the latter also belonging to the same category of LILs. We outline the progress made in the past years that has developed our understanding of the location and the efficient production of these emission lines. We have yet to realize the full potential of Ca II emission and its connection to the black hole and the BLR parameters which can be useful in - (1) the classification of Type-1 active galactic nuclei (AGNs) in the context of the main sequence of quasars, (2) to realize an updated radius-luminosity relation wherein the inclusion of the strength of this emission line with respect to H$\beta$ can be an effective tracer of the accretion rate of the AGN, and, (3) the close connection of Ca II to Fe II can allow us to use the ratio of the two species to quantify the chemical evolution in these active galaxies across cosmic time. In this paper, we use our current sample and utilize a non-linear dimensionality reduction technique - t-distributed Stochastic Neighbour Embedding (tSNE), to understand the clustering in our dataset based on direct observables.