Quenching of Star Formation

TL;DR

This paper introduces a robust PCA method for analyzing galaxy spectra, revealing the significance of post-starburst galaxies in galaxy evolution and comparing observational data with theoretical models.

Contribution

It presents a new iterative PCA algorithm for spectral analysis and applies it to identify post-starburst galaxies, linking them to galaxy evolution processes.

Findings

Identification of post-starburst galaxies at z~0.7 and z~0.07

Quantification of their role in building the red sequence

Comparison with semi-analytic models from Millennium Run

Abstract

In the last decade we have seen an enormous increase in the size and quality of spectroscopic galaxy surveys, both at low and high redshift. New statistical techniques to analyse large portions of galaxy spectra are now finding favour over traditional index based methods. Here we will review a new robust and iterative Principal Component Analysis (PCA) algorithm, which solves several common issues with classic PCA. Application to the 4000AA break region of galaxies in the VIMOS VLT Deep Survey (VVDS) and Sloan Digital Sky Survey (SDSS) gives new high signal-to-noise ratio spectral indices easily interpretable in terms of recent star formation history. In particular, we identify a sample of post-starburst galaxies at z~0.7 and z~0.07. We quantify for the first time the importance of post-starburst galaxies, consistent with being descendants of gas-rich major mergers, for building the red sequence. Finally, we present a comparison with new low and high redshift "mock spectroscopic surveys" derived from a Millennium Run semi-analytic model.