Extracting information from cosmic surveys is often done in a two-step
process, construction of maps and then summary statistics such as two-point
functions. We use simulations to demonstrate the advantages of a general
Bayesian framework that consistently combines different cosmological
experiments on the field level, and reconstructs both the maps and cosmological
parameters. We apply our method to jointly reconstruct the primordial CMB, the
Integrated Sachs Wolfe effect, and 6 tomographic galaxy density maps on the
full sky on large scales along with several cosmological parameters. While the
traditional maximum a posterior estimator has both 2-point level and
field-level bias, the new approach yields unbiased cosmological constraints and
improves the signal-to-noise ratio of the maps.
Description
A Field-Level Multi-Probe Analysis of the CMB, ISW, and the Galaxy Density Maps
%0 Generic
%1 zhou2023fieldlevel
%A Zhou, Alan Junzhe
%A Dodelson, Scott
%D 2023
%K bayesian_analysis cosmology machine_learning phd
%T A Field-Level Multi-Probe Analysis of the CMB, ISW, and the Galaxy
Density Maps
%U http://arxiv.org/abs/2304.01387
%X Extracting information from cosmic surveys is often done in a two-step
process, construction of maps and then summary statistics such as two-point
functions. We use simulations to demonstrate the advantages of a general
Bayesian framework that consistently combines different cosmological
experiments on the field level, and reconstructs both the maps and cosmological
parameters. We apply our method to jointly reconstruct the primordial CMB, the
Integrated Sachs Wolfe effect, and 6 tomographic galaxy density maps on the
full sky on large scales along with several cosmological parameters. While the
traditional maximum a posterior estimator has both 2-point level and
field-level bias, the new approach yields unbiased cosmological constraints and
improves the signal-to-noise ratio of the maps.
@misc{zhou2023fieldlevel,
abstract = {Extracting information from cosmic surveys is often done in a two-step
process, construction of maps and then summary statistics such as two-point
functions. We use simulations to demonstrate the advantages of a general
Bayesian framework that consistently combines different cosmological
experiments on the field level, and reconstructs both the maps and cosmological
parameters. We apply our method to jointly reconstruct the primordial CMB, the
Integrated Sachs Wolfe effect, and 6 tomographic galaxy density maps on the
full sky on large scales along with several cosmological parameters. While the
traditional maximum a posterior estimator has both 2-point level and
field-level bias, the new approach yields unbiased cosmological constraints and
improves the signal-to-noise ratio of the maps.},
added-at = {2023-07-22T18:11:44.000+0200},
author = {Zhou, Alan Junzhe and Dodelson, Scott},
biburl = {https://www.bibsonomy.org/bibtex/287ff2a7c79492910bc247a1e5226256a/intfxdx},
description = {A Field-Level Multi-Probe Analysis of the CMB, ISW, and the Galaxy Density Maps},
interhash = {509a0aab212988eb8f7ded479a631872},
intrahash = {87ff2a7c79492910bc247a1e5226256a},
keywords = {bayesian_analysis cosmology machine_learning phd},
note = {cite arxiv:2304.01387},
timestamp = {2023-07-22T18:11:44.000+0200},
title = {A Field-Level Multi-Probe Analysis of the CMB, ISW, and the Galaxy
Density Maps},
url = {http://arxiv.org/abs/2304.01387},
year = 2023
}