ForSE+: Simulating non-Gaussian CMB foregrounds at 3 arcminutes in a stochastic way based on a generative adversarial network
Astronomy & Astrophysics(2024)
摘要
We present ForSE+, a Python package that produces non-Gaussian diffuse
Galactic thermal dust emission maps at arcminute angular scales and that has
the capacity to generate random realizations of small scales. This represents
an extension of the ForSE (Foreground Scale Extender) package, which was
recently proposed to simulate non-Gaussian small scales of thermal dust
emission using generative adversarial networks (GANs). With the input of the
large-scale polarization maps from observations, ForSE+ has been trained to
produce realistic polarized small scales at 3' following the statistical
properties, mainly the non-Gaussianity, of observed intensity small scales,
which are evaluated through Minkowski functionals. Furthermore, by adding
different realizations of random components to the large-scale foregrounds, we
show that ForSE+ is able to generate small scales in a stochastic way. In both
cases, the output small scales have a similar level of non-Gaussianity compared
with real observations and correct amplitude scaling as a power law. These
realistic new maps will be useful, in the future, to understand the impact of
non-Gaussian foregrounds on the measurements of the cosmic microwave background
(CMB) signal, particularly on the lensing reconstruction, de-lensing, and the
detection of cosmological gravitational waves in CMB polarization B-modes.
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