TL;DR
ForSE+ is a Python tool that generates realistic, non-Gaussian small-scale Galactic dust emission maps at 3 arcminutes, aiding CMB analysis by simulating stochastic foregrounds based on GANs trained with observational data.
Contribution
It extends the ForSE package by incorporating a GAN-based approach to produce stochastic, non-Gaussian small-scale dust emission maps at high resolution, aligned with observed statistical properties.
Findings
Generated maps match observed non-Gaussianity levels.
Maps exhibit correct amplitude scaling as a power law.
Stochastic realizations effectively mimic real small-scale structures.
Abstract
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…
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