Short
Talk
Abstract
:Within cosmology component separation is instrumental in disentangling
information pertaining to the formation and evolution of the Universe
itself from astrophysical information describing the Universes key
constituents. Separating distinct, non-linear emission mechanisms
within large-scale data sets is a complex problem which can benefit
from a prior knowledge of said emission mechanisms. We present a new
model fitting technique which exploits the sparse nature of these
astrophysical emissions to provide more accurate model parameters in
less computational time than the current state-of-the-art
methodologies. This technique automatically identifies regions within
the data with common properties, makes fast, initial parameter
estimates and then refines these regional estimates to provide the
global solution through a least squares optimisation which favours
sparsity. We verify our method in the context of Planck simulation data.
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