The VIMOS Public Extragalactic Redshift Survey (VIPERS): PCA-based automatic cleaning and reconstruction of survey spectra

TL;DR

This paper introduces an automated PCA-based algorithm for identifying and repairing spurious features in galaxy spectra, significantly improving the efficiency and consistency of spectral cleaning in large surveys like VIPERS.

Contribution

The novel PCA-based method automates the detection and correction of sky residuals in galaxy spectra, reducing manual effort and ensuring uniform data quality in large spectroscopic surveys.

Findings

The algorithm accurately reproduces manual cleaning results.

It effectively identifies sky residuals at characteristic wavelengths.

Applied to 90,000 spectra, it provides consistent spectral quality.

Abstract

Identifying spurious reduction artefacts in galaxy spectra is a challenge for large surveys. We present an algorithm for identifying and repairing residual spurious features in sky-subtracted galaxy spectra with application to the VIPERS survey. The algorithm uses principal component analysis (PCA) applied to the galaxy spectra in the observed frame to identify sky line residuals imprinted at characteristic wavelengths. We further model the galaxy spectra in the rest-frame using PCA to estimate the most probable continuum in the corrupted spectral regions, which are then repaired. We apply the method to 90,000 spectra from the VIPERS survey and compare the results with a subset where careful editing was performed by hand. We find that the automatic technique does an extremely good job in reproducing the time-consuming manual cleaning and does it in a uniform and objective manner across a large data sample. The mask data products produced in this work are released together with the VIPERS second public data release (PDR-2).