Principal Component Analysis and Radiative Transfer modelling of Spitzer IRS Spectra of Ultra Luminous Infrared Galaxies

TL;DR

This study applies principal component analysis to Spitzer IRS spectra of ULIRGs, revealing key spectral features, comparing them with radiative transfer models, and proposing a new classification scheme based on spectral components.

Contribution

It identifies five principal components that effectively describe ULIRG spectra and introduces a novel spectral classification method using PCA and Gaussian mixture models.

Findings

Five principal components optimally describe ULIRG spectra.

PCA-based templates outperform radiative transfer models in spectral fitting.

Principal components relate to physical properties like power source and torus inclination.

Abstract

The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterise the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilise the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components. We further investigate the principal components (PCs) of ULIRGs derived in Wang et al.(2011). We quantitatively show that five PCs is optimal for describing the IRS spectra. These five components (PC1-PC5) and the mean spectrum provide a template basis set that reproduces spectra of all z<0.35 ULIRGs within the noise. For comparison, the spectra are also modelled with a combination of radiative transfer models of both starbursts and the dusty torus surrounding active galactic nuclei. The five PCs typically provide better fits than the models. We argue that the radiative transfer models require a colder dust component and have difficulty in modelling strong PAH features. Aided by the models we also interpret the physical processes that the principal components represent. The third principal component is shown to indicate the nature of the dominant power source, while PC1 is related to the inclination of the AGN torus. Finally, we use the 5 PCs to define a new classification scheme using 5D Gaussian mixtures modelling and trained on widely used optical classifications. The five PCs, average spectra for the four classifications and the code to classify objects are made available at: http://www.phys.susx.ac.uk/~pdh21/PCA/