Principal component analysis of the Spitzer IRS spectra of ultraluminous infrared galaxies

TL;DR

This study applies principal component analysis to Spitzer IRS spectra of 119 local ultraluminous infrared galaxies, revealing key factors like dust temperature, star formation, and AGN activity that characterize their spectral diversity.

Contribution

First PCA applied to ULIRG spectra, identifying principal components that capture over 90% of spectral variance and relate to physical properties.

Findings

First PC linked to dust temperature and geometry.

Second PC represents star formation activity.

Third PC shows anti-correlation between star formation and AGN presence.

Abstract

We present the first principal component analysis (PCA) applied to a sample of 119 Spitzer Infrared Spectrograph (IRS) spectra of local ultraluminous infrared galaxies (ULIRGs) at z<0.35. The purpose of this study is to objectively and uniquely characterise the local ULIRG population using all information contained in the observed spectra. We have derived the first three principal components (PCs) from the covariance matrix of our dataset which account for over 90% of the variance. The first PC is characterised by dust temperatures and the geometry of the mix of source and dust. The second PC is a pure star formation component. The third PC represents an anti-correlation between star formation activity and a rising AGN. Using the first three PCs, we are able to accurately reconstruct most of the spectra in our sample. Our work shows that there are several factors that are important in characterising the ULIRG population, dust temperature, geometry, star formation intensity, AGN contribution, etc. We also make comparison between PCA and other diagnostics such as ratio of the 6.2 microns PAH emission feature to the 9.7 micron silicate absorption depth and other observables such as optical spectral type.