A Bayesian estimate of the CMB-large-scale structure cross-correlation
E. Moura Santos, F. C. Carvalho, M. Penna-Lima, C. P. Novaes, C. A., Wuensche

TL;DR
This paper introduces a Bayesian method to estimate the cross-correlation between CMB temperature fluctuations and large-scale structure, aiding in understanding dark energy and cosmic acceleration.
Contribution
It presents a novel Bayesian sampling approach to measure CMB-LSS cross-correlation, reducing computational complexity compared to traditional methods.
Findings
Cosmic variance dominates the weak signal detection.
Shallowness of galaxy catalog limits signal strength.
Sampling systematics are secondary sources of uncertainty.
Abstract
Evidences for late-time acceleration of the Universe are provided by multiple probes, such as Type Ia supernovae, the cosmic microwave background (CMB) and large-scale structure (LSS). In this work, we focus on the integrated Sachs--Wolfe (ISW) effect, i.e., secondary CMB fluctuations generated by evolving gravitational potentials due to the transition between, e.g., the matter and dark energy (DE) dominated phases. Therefore, assuming a flat universe, DE properties can be inferred from ISW detections. We present a Bayesian approach to compute the CMB--LSS cross-correlation signal. The method is based on the estimate of the likelihood for measuring a combined set consisting of a CMB temperature and a galaxy contrast maps, provided that we have some information on the statistical properties of the fluctuations affecting these maps. The likelihood is estimated by a sampling algorithm,…
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