Font Size: a A A

Foreground Removal For Cosmic Microwave Background

Posted on:2021-10-06Degree:MasterType:Thesis
Country:ChinaCandidate:J YaoFull Text:PDF
GTID:2480306503986969Subject:Astronomy major
Abstract/Summary:PDF Full Text Request
As the most ancient light in the universe,the Cosmic Microwave Background(CMB)contains a lot of information about the early universe and the late-time evolution,which triggered many interesting cosmological phenomena.By studying the rich information brought by CMB,we will be able to find the hidden treasure behind it.The most fascinating pearl is the primordial gravitational wave.This gravitational wave generated in the early universe by the Inflation theory has left a hidden clue in the CMB signal,waiting for us to discover.In addition,CMB's scientific goals include constraint for the neutrino mass,measurement of the Hubble constant,etc.Once accurately measured,they will provide an important thrust for the development of modern cosmology.The realization of these scientific goals depends on a prerequisite: we can actually get the clean CMB signal.But the fact is that we observe not only CMB signals,but also foreground signals that are much stronger than CMB.These strong interference foreground almost completely cover CMB signals behind them.Therefore,we need to use some specific foreground subtraction methods to be able to extract the CMB signal,either the CMB sky map or the CMB statistical information,from the observed total signal.The main work of this paper is to use an analytical foreground subtraction algorithm,called ABS,to test against the simulated and actual observed temperature sky map,assuming some severe conditions,and obtain stable and unbiased recovered results.It is further used in the recovery of CMB polarization signals,and accurate results are obtained,indicating that this method will have a place among the foreground removal methods for future CMB signal detection experiments.
Keywords/Search Tags:Cosmic Microwave Background, Foreground removal methods, Analytical method of Blind Separation, Data analysis
PDF Full Text Request
Related items