ForSE: A GAN-based Algorithm for Extending CMB Foreground Models to Subdegree Angular Scales - INSPIRE

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ForSE: A GAN-based Algorithm for Extending CMB Foreground Models to Subdegree Angular Scales

Nicoletta Krachmalnicoff
(
- SISSA, Trieste and
- INFN, Trieste and
- IFPU, Trieste
)
,
Giuseppe Puglisi
(
- UC, Berkeley, Space Sci. Dept. and
- LBNL, Berkeley
)

Nov 4, 2020

14 pages

Published in:

Astrophys.J. 911 (2021) 1, 42

Published: Apr 14, 2021

e-Print:

2011.02221 [astro-ph.CO]

DOI:

10.3847/1538-4357/abe71c

View in:

ADS Abstract Service

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Abstract: (IOP)

We present ForSE (Foreground Scale Extender), a novel Python package that aims to overcome the current limitations in the simulation of diffuse Galactic radiation, in the context of cosmic microwave background (CMB) experiments. ForSE exploits the ability of generative adversarial neural networks (GANs) to learn and reproduce complex features present in a set of images, with the goal of simulating realistic and non-Gaussian foreground radiation at subdegree angular scales. This is of great importance in order to estimate the foreground contamination to lensing reconstruction, delensing, and primordial B-modes for future CMB experiments. We applied this algorithm to Galactic thermal dust emission in both total intensity and polarization. Our results show how ForSE is able to generate small-scale features (at 12′) having as input the large-scale ones (80′). The injected structures have statistical properties, evaluated by means of the Minkowski functionals, in good agreement with those of the real sky and which show the correct amplitude scaling as a function of the angular dimension. The obtained thermal dust Stokes Q and U full-sky maps as well as the ForSE package are publicly available for download.

Note:

Version 2

Cosmic microwave background radiation
Neural networks
Diffuse radiation
B-mode: primordial
cosmic background radiation
thermal
galaxy
non-Gaussianity
neural network
statistical analysis

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