Reliable Eigenspectra for New Generation Surveys

TL;DR

This paper introduces a robust, recursive eigenspectrum estimation method that effectively handles outliers, missing data, and high-dimensional astronomical spectra, improving upon traditional PCA techniques.

Contribution

It presents a novel approach combining robust statistics and recursive algorithms to derive reliable eigenspectra without manual data curation, scalable to large datasets.

Findings

Demonstrated on VIMOS VLT Deep Survey spectra with improved eigenspectra quality.

Outperforms previous methods in handling outliers and missing data.

Scalable to large surveys like SDSS.

Abstract

We present a novel technique to overcome the limitations of the applicability of Principal Component Analysis to typical real-life data sets, especially astronomical spectra. Our new approach addresses the issues of outliers, missing information, large number of dimensions and the vast amount of data by combining elements of robust statistics and recursive algorithms that provide improved eigensystem estimates step-by-step. We develop a generic mechanism for deriving reliable eigenspectra without manual data censoring, while utilising all the information contained in the observations. We demonstrate the power of the methodology on the attractive collection of the VIMOS VLT Deep Survey spectra that manifest most of the challenges today, and highlight the improvements over previous workarounds, as well as the scalability of our approach to collections with sizes of the Sloan Digital Sky Survey and beyond.