Quantifying correlations between galaxy emission lines and stellar continua

TL;DR

This paper investigates the relationship between galaxy emission lines and stellar continua, proposing methods to improve spectral modelling and galaxy classification for upcoming large sky surveys.

Contribution

It introduces a technique to reconstruct emission line equivalent widths from stellar continuum data using PCA and regression, enhancing galaxy spectral models and classifications.

Findings

Emission lines can be reconstructed from continuum using PCA and regression.

The method improves spectral modelling for star-forming and active galaxies.

A classification approach distinguishes AGN hosts from star-forming galaxies.

Abstract

We analyse the correlations between continuum properties and emission line equivalent widths of star-forming and active galaxies from the Sloan Digital Sky Survey. Since upcoming large sky surveys will make broad-band observations only, including strong emission lines into theoretical modelling of spectra will be essential to estimate physical properties of photometric galaxies. We show that emission line equivalent widths can be fairly well reconstructed from the stellar continuum using local multiple linear regression in the continuum principal component analysis (PCA) space. Line reconstruction is good for star-forming galaxies and reasonable for galaxies with active nuclei. We propose a practical method to combine stellar population synthesis models with empirical modelling of emission lines. The technique will help generate more accurate model spectra and mock catalogues of galaxies to fit observations of the new surveys. More accurate modelling of emission lines is also expected to improve template-based photometric redshift estimation methods. We also show that, by combining PCA coefficients from the pure continuum and the emission lines, automatic distinction between hosts of weak active galactic nuclei (AGNs) and quiescent star-forming galaxies can be made. The classification method is based on a training set consisting of high-confidence starburst galaxies and AGNs, and allows for the similar separation of active and star-forming galaxies as the empirical curve found by Kauffmann et al. We demonstrate the use of three important machine learning algorithms in the paper: k-nearest neighbour finding, k-means clustering and support vector machines.