Needlet estimation of cross-correlation between CMB lensing maps and LSS

TL;DR

This paper introduces a needlet-based estimator for cross-correlating CMB lensing maps with large-scale structure data, improving bias handling and signal-to-noise ratio for future cosmological studies.

Contribution

It develops a novel needlet estimator for CMB-LSS cross-correlation, compares it with harmonic methods, and implements bias correction techniques for enhanced analysis.

Findings

Needlet estimator shows superior signal-to-noise performance.

Bias effects due to masking are effectively mitigated.

Method is optimized for next-generation cosmological experiments.

Abstract

In this paper we develop a novel needlet-based estimator to investigate the cross-correlation between cosmic microwave background (CMB) lensing maps and large-scale structure (LSS) data. We compare this estimator with its harmonic counterpart and, in particular, we analyze the bias effects of different forms of masking. In order to address this bias, we also implement a MASTER-like technique in the needlet case. The resulting estimator turns out to have an extremely good signal-to-noise performance. Our analysis aims at expanding and optimizing the operating domains in CMB-LSS cross-correlation studies, similarly to CMB needlet data analysis. It is motivated especially by next generation experiments (such as Euclid) which will allow us to derive much tighter constraints on cosmological and astrophysical parameters through cross-correlation measurements between CMB and LSS.