The VIMOS Public Extragalactic Redshift Survey (VIPERS): spectral classification through Principal Component Analysis

TL;DR

This paper presents a PCA-based method for classifying galaxy spectra from VIPERS, effectively condensing spectral data into key coefficients linked to galaxy properties, and producing a comprehensive spectral template catalog.

Contribution

It introduces an iterative PCA approach for spectral classification, reconstructs missing data, and creates a spectral template set that captures galaxy diversity in a compact form.

Findings

Spectral templates describe galaxy types with two key coefficients.

The method successfully classifies galaxies into early, intermediate, late, and starburst types.

The approach improves spectral data analysis by handling noise and gaps effectively.

Abstract

We develop a Principal Component Analysis aimed at classifying a sub-set of 27,350 spectra of galaxies in the range 0.4 < z < 1.0 collected by the VIMOS Public Extragalactic Redshift Survey (VIPERS). We apply an iterative algorithm to simultaneously repair parts of spectra affected by noise and/or sky residuals, and reconstruct gaps due to rest-frame transformation, and obtain a set of orthogonal spectral templates that span the diversity of galaxy types. By taking the three most significant components, we find that we can describe the whole sample without contamination from noise. We produce a catalogue of eigen-coefficients and template spectra that will be part of future VIPERS data releases. Our templates effectively condense the spectral information into two coefficients that can be related to the age and star formation rate of the galaxies. We examine the spectrophotometric types in this space and identify early, intermediate, late and starburst galaxies.