Extracting key information from spectroscopic galaxy surveys

TL;DR

This paper introduces a combined PCA and MOPED method to efficiently extract and compress key cosmological information from spectroscopic galaxy surveys, improving parameter constraints over traditional techniques.

Contribution

The paper presents a novel joint PCA and MOPED approach for extracting and compressing cosmological information from galaxy survey data, enhancing constraint accuracy.

Findings

PCA effectively extracts informative modes from the 2D correlation function.

The method improves the Figure of Merit for BAO and RSD parameters by 17%.

Data compression reduces the data dimension with minimal loss of information.

Abstract

We develop a novel method to extract key cosmological information, which is primarily carried by the baryon acoustic oscillations and redshift space distortions, from spectroscopic galaxy surveys based on a joint principal component analysis (PCA) and massive optimized parameter estimation and data compression (MOPED) algorithm. We apply this method to galaxy samples from BOSS DR12, and find that a PCA manipulation is effective at extracting the informative modes in the 2D correlation function, giving a tighter constraint on BAO and RSD parameters compared to that using the lowest three multipole moments by the traditional method; i.e. the Figure of Merit of BAO and RSD parameters is improved by $17\%$. We then perform a compression of the informative PC modes for BAO and RSD parameters using the MOPED scheme, reducing the dimension of the data vector to the number of interesting parameters, manifesting the joint PCA and MOPED as a powerful tool for clustering analysis with almost no loss of constraining power.